Uncertainty Factors Research Articles

Identification of sensitive endpoints for the assessment of phthalates-induced reproductive and developmental toxicity: A literature mining study

Dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP), two common types of phthalates, are known to cause reproductive and developmental toxicity in animals and humans. The reference doses (RfD) of DBP and DEHP should be determined by sensitive endpoints. We here aimed to identify sensitive endpoints for DBP- and DEHP-induced such toxicity using published literatures. By examining the impacts of maternal exposure to DBP or DEHP on anogenital distance (AGD) and semen quality of offspring, we discovered that DBP or DEHP caused AGD decline in boys but increase in girls with DBP being more potent and the first 14weeks of pregnancy being more susceptible, suggesting a chemical- and time-dependent phenomenon. We also identified AGD shortening and total sperm count reduction as two sensitive endpoints for DBP- or DEHP-induced reproductive and developmental toxicity, respectively. Based upon these two endpoints and the employment of the Bayesian benchmark dose approach with an uncertainty factor of 3,000, we estimated the RfD values of DBP and DEHP were 15 μg/kg/day and 36 μg/kg/day, respectively. Thus, we uncover previously unrecognized phenomena of DBP- or DEHP-induced reproductive and developmental toxicity and establish new and comparable or more conservative RfDs for the risk assessment of phthalates exposure in humans.

E-waste circular economy decision-making: a comprehensive approach for sustainable operation management in the UK

E-waste generation has broadly increased worldwide and is called intense pressure on sustainable practice implementation firms by recycling and redesigning the products. Thus, e-waste operation management in developed countries like the UK has become the top priority and is subjected to multiple sustainable circular economies (CE) contributing factors, including social, technical, environmental, and governmental policies. The authorized decision-makers can benefit from a well-established systematic decision-making tool to assess and evaluate the e-waste operation management considering the potential CE contributing factors. An extensive literature overview is expanded to identify the most relevant and influential contributing factors to e-waste CE. The city of London Metropolitan has been selected as the case location. In this regard, it is necessary to utilize an advanced multi-criteria decision-making tool to explore the interdependency and causality of CE-relevant factors. The present study proposed an innovative decision-making approach to address the multiple contributing factors of causality, interdependency, data, and model uncertainty in practice. It uses the step-wise weighted influence nonlinear gauge system method integrated with Fermatean fuzzy linguistic sets. This study conducted a sensitivity analysis to evaluate the effectiveness of the proposed decision-making approach in e-waste operation management. The results are promising, clearly demonstrating the framework’s competence. The CE index, crucial in designing e-waste operation management strategies, was calculated to be 2.8036. Among the various factors analyzed, “Environmental Management Systems” emerged as the most significant driving factor. This underscores the critical need to improve environmental management systems within e-waste operations.

FACTORS RELATED TO ANXIETY LEVEL IN ELDERLY RETIREMENTS IN SEROJA PUSKESMAS, BEKASI CITY 2022

Background: Aging is a process of slowly disappearing – gradually the ability of tissues to repair themselves or maintain their normal function so that they cannot survive the infection and repair the damage suffered. Anxiety the appearance of a fear or worry in something unclear and related to feelings of uncertainty and helplessness Factors causinganxiety in the elderly . Internal factors (biological, psychological, socio-cultural) : external factors (gender, age, social support, economic status). Researcher's Objective:Knowing the factors related to the level of anxiety in the retired elderly at the Seroja Kota Bekasi Health Center 2022. Research Method: The design in this study used Observational Analytics with a Cross Sectional research design, using independent variables of factors causing elderly packaging and a type of random sampling with a total sample of 88 respondents. The instruments in this research used digital questionnaires and interviews and were conducted using Chi Square. Researcher results:Based on statistical analysis with a significance level of 95% or a α value (0.05) chi square test results obtained p value (0.562) > α value (0.05) the results did not have a significant relationship between sex and anxiety level in respondents this showed Ha was rejected, 95% significance level or α value (0.05) Chi Square test results obtained p value (0.134) > α value (0.05) so that it can be concluded from these results there is no significant relationship between age and anxiety level in respondents this shows Ha is rejected, a significance level of 95% or a α value (0.05) chi square test results obtained p value (0.000) < α value (0.05) so that it can be concluded from these results there is a relationship between economic status and anxiety level in respondents this shows H0 is rejected, with a significance level of 95% or a value of α (0.05) chi square test results obtained p value (0.000) < value α (0.05) so that it can be concluded from these results there is a relationship between family support and the level of anxiety in respondents this shows that H0 is rejected. Thus it can be interpreted that "factors related to the level of anxiety in elderly retirees at the Seroja K Health Center ota Bekasi 2022". Conclusion: there is a relationship and there is no relationship between external factors, the level of anxiety level in the retired elderly at the Bekasi Health Center 2022.

Risk-averse distributed optimization for integrated electricity-gas systems considering uncertainties of Wind-PV and power-to-gas

The uncertainties of wind power and photovoltaic (Wind-PV) systems pose a threat to the steady functioning of integrated electricity-gas systems and restrict their effective consumption. In this case, an optimal operation model considering the cost risk of Wind-PV uncertainties is proposed. Firstly, the cost risk of uncertainties in typical scenarios is measured by conditional value at risk (CVaR). Secondly, considering that the power system and the natural gas system as different stakeholders have barriers in information exchange, a distributed optimization operation model is established, and the uncertainty of the conversion efficiency of the coupling unit from Power-to-Gas (P2G) is considered. Then, the integrated electricity-gas systems are decoupled through consistency constraints, and the model is solved by an algorithm framework based on adaptive penalty ADMM. This work analyzed the impact of uncertainty and CVaR parameters on optimized operation, and discussed the performance of related distributed algorithms. The following conclusions were drawn: 1) the proposed model reduces costs by 1.72% and can measure the operation risk caused by uncertainty factors, which provide guidance for decision makers; 2) the total cost will increase by 3.9% when the uncertainty factor reaches the preset maximum value, which demonstrates that the uncertainty of conversion efficiency should be controlled to reduce the operation cost of P2G; 3) As the convergence accuracy gradually improves, the proposed method is easier to converge and converges faster.

VIKOR METHOD WITH Z-NUMBER APPROACH FOR PORTFOLIO SELECTION DECISION

Investors and decision makers (DMs) have become increasingly interested in portfolio selection in a borderless world in recent years. In real-world market situations, the performance of a great number of portfolios is typically unpredictable due to the presence of uncertainty and unreliable factors in numerous criteria. Therefore, it is essential to increase investor returns and promote an investment strategy through thorough evaluation. This occurrence becomes critical if the DMs employ an unsuitable strategy that fails to handle both aspects in a prudent manner. Due to its importance, this paper implements a VIKOR method with a Z-number approach for selecting the optimal portfolio among the identified alternatives. It is believed that the two components A and B of the Z-number structure, where A is a restriction of the evaluated attribute and B is a degree of certainty of A, deal with uncertainties and reliability issues more effectively. A numerical example from an adopted case study has been provided to demonstrate the effectiveness and viability of the proposed method. The outcome demonstrates that the approach can address the uncertainty of human judgement with greater precision while simultaneously boosting the DMs' confidence throughout the evaluation process. Consequently, the proposed method provides a more dependable and effective method for DMs to make decisions, particularly regarding portfolio selection.

RISKS IDENTIFYING AND ASSESSMENT PECULIARITIES IN BUSINESS PLANNING PROCESS

The scientific article discusses problematic aspects of identifying and assessing risks at the stage of developing business plans. It was determined that changes in the economic environment of enterprises require the formation of new approaches and means of solving economic problems. One of the effective ways to solve this problem is to develop a business plan. This document should be considered both a management tool of the business entity and a means of attracting financing. The specifics of business planning are defined, the main reasons that lead to the development of a business plan are considered.
 It is noted that risks are an important element that must be paid attention to in business planning. The interpretation of the term "risk" in the scientific literature is generalized. It was determined that the presence of risk in the business plan is a forming factor of uncertainty. The study points to the presence of risks as a mandatory and undeniable element of enterprise activity in modern conditions. Risk assessment is a mandatory structural element of a typical business plan.
 It is indicated that each business plan is individual and unique. It is proposed to detail the risks not in a separate section "Risks", but in the section of the main sections of the business plan: market analysis, marketing plan, organizational plan and management, production plan, financial plan, which will allow group all existing risks, detail them and develop priority measures to neutralize them.
 The main types of risks that may arise in the main sections of the business plan are revealed. It was determined that the systematization of types and groups of risks by sections is an important tool for their effective management, because it helps to choose individual approaches to assessment, measurement, forecasting and selection of response measures.
 As a result, it is proposed to form an appropriate table of risks and means of their neutralization, which will allow to show the investor or an interested person that the applicant is informed and able to cope with the risks and threats of the project

Prediction Intervals for Bus Travel Time Based on Road Segment Sharing, Multiple Routes’ Driving Style Similarity, and Bootstrap Method

Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes’ driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively.

Multiview Type-2 kernelized fuzzy C-means clustering with local information for noisy color image segmentation

Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image.

The importance of technology and cold chain logistics in Vietnam's preservation of harvested seafood

Vietnam has been experiencing significant economic growth in recent years, making it an attractive destination for foreign investors. With globalization and the expansion of trade relationships, logistics management has become crucial for domestic and multinational companies operating in Vietnam. However, logistics operations in Vietnam are exposed to various risks, such as natural disasters, infrastructure issues, regulatory uncertainty, and human factors. Therefore, it is essential to manage logistics risk effectively to ensure the supply chain's resilience and the business's sustainability. This paper aims to provide a critical review of the risk management literature in logistics by analyzing the key concepts of supply chain risk, the principles of logistic risk management, types of logistics risks, and some ideas on solutions in risk management in the supply chain. Keywords: Supply chain, risk management, Vietnam

Estimating specific acute exposure guideline levels for emergency response at the scene of chemical accidents: A case study of acrylic acid

Acute Exposure Guideline Levels (AEGLs) proposed by the United States Environmental Protection Agency (USEPA) are reference values for disaster management during scenarios involving chemical accidents. We attempted to specify the present AEGLs of acrylic acid that are provided with only five intervals (10, 30 min, 1, 4, and 8 h) as a brief case study. The AEGL-1, 2, 3 values for acrylic acid were estimated at 96 intervals (5 min–8 h) in detail using the revised Haber's formula (k = Cn × t) and applying a regression model. The AEGL-1 value was constant for the entire exposure duration of 1.5 ppm since it caused only slight irritation without irreversible health effects. The AEGL-2 value was obtained with fixed values (n = 1.8, uncertainty factor of 3, and each 5 min interval), with that for the initial 5 min being derived as 183 ppm, which decreased to 43 ppm after 8 h of exposure. The AEGL-3 values fluctuated because of the risk to human health. The AEGL-3 for the initial 5 min exposure was derived as 696 ppm, which decreased to 58 ppm after 8 h exposure applied to the regression model. Specific AEGL values for acrylic acid were derived using toxicity data and the revised Haber's formula. By applying the methodology described in this study, the reference values for chemical substances that have no AEGL values can be calculated to potentially enhance emergency response in the event of chemical accidents.

Dynamic vulnerability assessment of hybrid system considering wind power uncertainty

The stochastic volatility of wind power introduces numerous uncertainties to the security and stability of the hybrid system, which in turn affects the overall vulnerability level of the system. In this study, a wind power output model incorporating the effects of wind abandonment and uncertainty factors is established. Additionally, a hybrid optimal power flow (OPF) method considering economic dispatch, stochastic trip, and islanding strategies is developed to simulate the power flow dispatch during cascading failures. Furthermore, a set of indexes describing the global dynamic vulnerability of the hybrid system is proposed to reveal the effects of changes in wind power uncertainty and penetration levels on the hybrid system in the short term. Simulations were conducted on an adapted IEEE118 system, and the results demonstrate that an increase in wind power uncertainty and penetration will exacerbate the system’s vulnerability. This severity is particularly pronounced during peak and valley load periods, making the hybrid system more susceptible to large-scale outages. When the wind power uncertainty level grows to 4, the overall vulnerability level of the hybrid system increases by 102.3% compared to the initial uncertainty case. When the wind penetration level reaches 46%, the overall vulnerability level rises by 75.9% compared to the initial system. These findings emphasize the sensitivity of the hybrid system to different levels of wind power uncertainties and penetrations, providing evidence-based support for the prevention of system cascade failures.

Reinforcement learning in autonomous driving

Automatic driving technology has become a highly researched field in recent years, aiming to achieve vehicle driving without human intervention. In this regard, reinforcement learning techniques have played a crucial role. This study discusses and analyses the use of reinforcement learning in automatic driving methods. The research begins with the process of reinforcement learning. In the architectural framework, there is a special emphasis on designing innovative reward functions to encourage safe and socially acceptable driving behaviour, while considering uncertainty factors through advanced Bayesian neural networks. This paper primarily focuses on aspects such as scene understanding, localization and mapping, planning and driving strategies, and control. Furthermore, the paper analyses the key elements of automatic driving and delves into the specific complexities associated with each element. It highlights the utilization of reinforcement learning within the realm of autonomous driving. Reinforcement learning assists autonomous vehicles in understanding the surrounding environment, accurately identifying paths, making intelligent driving decisions, and safely controlling the vehicle. Reinforcement learning especially working with deep learning plays a crucial role in realizing and continuously improving automatic driving. Finally, the paper concludes with a summary and outlook on the entire study.

Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors

Disc buckle steel pipe brackets are widely used in building construction due to the advantages of its simple structure, large-bearing capacity, rapid assembling and disassembling, and strong versatility. In complex construction projects, the uncertainties affecting the stability of disc buckle steel pipe support need to be considered to ensure the safety of disc buckle steel pipe supports. A surrogate model based on a deep neural network is built and trained to predict the ultimate load-carrying capacity of a stent. The results of the finite element model calculations are used to form the sample set of the surrogate model. Then, we combined the computationally efficient DNN surrogate model with the Monte Carlo method to consider the distribution of the ultimate load capacity of the disc buckle bracket under the uncertainties of the bracket node pin wedge tightness, the wall thickness of the steel pipe, and the connection of the connecting wall member. At the same time, based on the DNN model, the SHapley Additive exPlanations (SHAP) interpretability analysis method was used to study the degree of influence of various uncertainty factors on the ultimate bearing capacity of the stent. In practical engineering, the stability analysis of a disc buckle tall formwork support has shown that a surrogate model based on a deep neural network is efficient in predicting the buckling characteristic value of the support. The error rate of the prediction is less than 2%. The buckling characteristic values of the bracket vary in the range of 17–25. Among the various factors that influence the buckling characteristic value of the bracket, the joint wedge tightness has the greatest impact, followed by the bottom and top wall-connecting parts.

A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.

Efficiency of investment activity in the dairy subcomplex

The article presents the results related to the evaluation of the effectiveness of investment activities in the dairy subcomplex. It is established that investments are an important component of the economic development of the dairy subcomplex, the effectiveness of which depends on a set of factors of the macro- and microeconomic environment, its organizational and economic characteristics, the investment strategy of enterprises, objects and sources of investment, the period for which capital is invested, the certainty of its future effect and the associated risk level. It was found out that in the conditions of low competitiveness of dairy subcomplex enterprises, there is a need to use effective investment projects, which significantly strengthened the role of investment efficiency analysis as a tool within which it is possible to justify, evaluate and select priority investment projects. It is determined that the task of analyzing the effectiveness of investments is the calculation of profits and cash flows, the assessment of their effectiveness, the identification of the influence of uncertainty factors on their profitability, the selection of optimal investment projects. It is advisable to organize the analysis of the effectiveness of investments in the following areas: the definition of subjects and objects of analysis; the choice of organizational forms of analysis, depending on the organizational structure and the distribution of responsibilities between individual performers; the preparation of the analysis program and its information support; analytical data processing, design and generalization of the analysis results. The proposed methodological approach to the analysis of investment efficiency takes into account the conditions of an unstable market environment (variable macroeconomic conditions, insufficient supply of dairy raw materials, uncertainty about wholesale purchase and sale prices), makes it possible to determine the effectiveness of investments, conduct risk analysis by simulation and predict data for risk analysis using regression methods.

The Co-volatility of Uncertainty Shock on Chinese Exchange Rate: Based on Time-frequency Domain and Time-varying Method

In recent years, with the global epidemic, the Russia-Ukraine conflict, the great power game, and so on events, the international political and economic environment has become more and more unstable, the USDCNY exchange rate fluctuates significantly, and the external uncertainty risk shocks negatively hit the foreign exchange market. Based on this, this article expands global uncertainty risk shocks into four dimensions: global geopolitical risk, U.S. economic policy uncertainty, Chinese economic policy uncertainty, and VIX index, in addition to adding international WTI crude oil and gold price as additional variables, which are significantly correlated with both, to study the linkage among uncertainty shocks, commodity volatility and USDCNY exchange rate from the time-frequency domain. Furthermore, we analyze the time-varying spillover effects among variables at different frequency scales by decomposing the data into multiple scales using the Maximum Overlap Discrete Wavelet Transform (MODWT). Findings reveal that uncertainty factors and commodity prices exert distinct influence mechanisms on the USDCNY exchange rate under different cycles of frequency fluctuations. At various time-frequency domain volatility scales, VIX and WTI generate significant risk spillovers over the USDCNY exchange rate. Moreover, when global major events occur, time-varying spillover effects of variables are also heterogeneous. The research findings presented in this paper can provide valuable insights for policymakers and investors regarding the impact of international uncertainty shocks on the Chinese exchange rate across various short, medium, and long frequencies. This understanding can aid in managing uncertainty risks in decision-making processes, ultimately facilitating the efficient functioning of the foreign exchange market.

Evaluation of MUF uncertainty based on GUM method for benchmark bulk handling facility

The Republic of Korea is performing independent national inspections under the IAEA's State System of Accounting for and Control (SSAC), and developing an evaluation methodology for the material unaccounted for (MUF) to reinforce capabilities with the purpose of assessment for the accounting system of the facility handling bulk nuclear materials. In relation to this, a new approach for MUF evaluation was proposed in this study based on the guide to the expression of uncertainty in measurement (GUM). Both the conventional MUF evaluation method and the GUM method were applied to a hypothetical list of inventory items including material balance. Considering the ease of uncertainty propagation according to the GUM, it was assumed that independent uncertainty factors correspond to random factors, while correlated uncertainty factors correspond to systematic factors. The total MUF uncertainties were similar for both methods; however, it was verified that some uncertainties were affected by the measurement procedure in the GUM method. Furthermore, the GUM method was found to be more conducive to conducting a factor analysis for the MUF uncertainty. It was therefore concluded that application of the GUM approach could be beneficial in cases of national safeguard inspections where factor analysis is required for MUF assessment.

Hydrogen refueling station location optimization under uncertainty

Hydrogen refueling stations (HRS) are essential for the growth of the hydrogen energy and fuel cell vehicle industry, making the strategic decision of their location and scale critical. This study advances the HRS location optimization problem by integrating network planning and hydrogen scheduling optimization, a novel approach that addresses both where and how big HRSs should be, alongside managing supply relations and hydrogen transportation scheduling. Uniquely, it incorporates uncertainty factors in hydrogen demand, using a two-stage multi-period stochastic programming model designed to minimize both construction and operational costs under variable demand scenarios, while also ensuring customer satisfaction. Our main contributions include the development of this comprehensive model and the introduction of an efficient metaheuristic algorithm to solve it, significantly enhancing computational efficiency. The effectiveness of our approach is demonstrated through numerical experiments, and a sensitivity analysis provides crucial insights, underlining the practical implications for HRS infrastructure development. The findings not only offer a methodological advancement in HRS location selection but also deliver tangible results that can guide industry stakeholders in strategic decision-making, marking a significant step forward in the sustainable development of hydrogen energy infrastructure.

Estimation and usefulness of measurement uncertainty from sampling at different spatial scales: microns to kilometres

Uncertainty of measurement values (MU) is crucial to their reliable geochemical interpretation. MU can be estimated using the Duplicate Method, which requires the taking of a small proportion of duplicated samples, and can be applied at any spatial scale. The distance between the duplicated samples is selected to reflect the effect of analyte heterogeneity on the measurement result (i.e. estimated concentration) within each sampling target, at the particular scale of investigation. Three published case studies, at different spatial scales, are used to explain how the Duplicate Method can be applied to estimate MU. They also illustrate how MU can be used to improve geochemical interpretation and validate measurement procedures (that include sampling) by judging their fitness for purpose. At the kilometre scale, measurements from the GEMAS survey of agricultural soils across Europe are used to estimate their MU for the first time. The MU for 53 elements range from an uncertainty factor of 1.01 to over 10. The MU contributes more that 20% to the total variance for 8 of the 53 elements, showing that the measurement procedure was not fit for purpose in those cases. At the micron scale, measurements of oxygen isotopes in candidate quartz reference materials had MU that was dominated by its sampling component, caused by sometimes unacceptable heterogeneity. A third case study of Pb in soils at 12 UK sites showed that the Duplicate Method can also be used to quantify the heterogeneity (as factor 1.03 to 2.4), and that it can indicate different possible sources of an element.

A Review of Cyclic Imines in Shellfish: Worldwide Occurrence, Toxicity and Assessment of the Risk to Consumers.

Cyclic imines are a class of lipophilic shellfish toxins comprising gymnodimines, spirolides, pinnatoxins, portimines, pteriatoxins, prorocentrolides, spiro-prorocentrimine, symbiomines and kabirimine. They are structurally diverse, but all share an imine moiety as part of a bicyclic ring system. These compounds are produced by marine microalgal species and are characterized by the rapid death that they induce when injected into mice. Cyclic imines have been detected in a range of shellfish species collected from all over the world, which raises the question as to whether they present a food safety risk. The European Food Safety Authority (EFSA) considers them to be an emerging food safety issue, and in this review, the risk posed by these toxins to shellfish consumers is assessed by collating all available occurrence and toxicity data. Except for pinnatoxins, the risk posed to human health by the cyclic imines appears low, although this is based on only a limited dataset. For pinnatoxins, two different health-based guidance values have been proposed at which the concentration should not be exceeded in shellfish (268 and 23 µg PnTX/kg shellfish flesh), with the discrepancy caused by the application of different uncertainty factors. Pinnatoxins have been recorded globally in multiple shellfish species at concentrations of up to 54 times higher than the lower guidance figure. Despite this observation, pinnatoxins have not been associated with recorded human illness, so it appears that the lower guidance value may be conservative. However, there is insufficient data to generate a more robust guidance value, so additional occurrence data and toxicity information are needed.