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As the global climate changes, there is an increasing focus on the oceans and their protection and exploitation. However, the exploration of the oceans necessitates the construction of marine equipment, and the siting of such equipment has become a significant challenge. With the ongoing development of computers, machine learning using remote sensing data has proven to be an effective solution to this problem. This paper reviews the history of remote sensing technology, introduces the conditions required for site selection through measurement analysis, and uses cluster analysis methods to identify areas such as machine learning as a research hotspot for ocean remote sensing. The paper aims to integrate machine learning into ocean remote sensing. Through the review and discussion of this article, limitations and shortcomings of the current stage of ocean remote sensing are identified, and relevant development proposals are put forward. [ABSTRACT FROM AUTHOR]

The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Runtime monitors for example check that the data at runtime is compatible with the data used to train the model. In a first step towards identifying challenges when specifying requirements for training data and runtime monitors, we conducted and thematically analysed ten interviews with practitioners who develop ML models for critical applications in the automotive industry. We identified 17 themes describing the challenges and classified them in six challenge groups. In a second step, we found interconnection between the challenge themes through an additional semantic analysis of the interviews. We explored how the identified challenge themes and their interconnections can be mapped to different architecture views. This step involved identifying relevant architecture views such as data, context, hardware, AI model, and functional safety views that can address the identified challenges. The article presents a list of the identified underlying challenges, identified relations between the challenges and a mapping to architecture views. The intention of this work is to highlight once more that requirement specifications and system architecture are interlinked, even for AI-specific specification challenges such as specifying requirements for training data and runtime monitoring. [ABSTRACT FROM AUTHOR]

Abstract Background Celiac disease is a major public health problem in many countries, including Iran. Considering the disease’s exponential spread throughout the world and its risk factors, identifying the educational priorities and minimum data required to control and treat the disease is of great significance. Methods The present study was conducted in two phases in 2022. In the first phase, a questionnaire was developed based on the information obtained from a review of the literature. Later, the questionnaire was administered to 12 pundits in the fields of nutrition (n = 5), internal medicine (n = 4), and gastroenterology (n = 3). As a result, the necessary and important educational content was determined for developing the Celiac Self-Care System. Results According to the experts’ viewpoints, the educational needs of patients were classified into nine categories of demographic information, clinical information, long-term complications, comorbidity, tests, medications, dietary recommendations, general recommendations, technical capabilities as well as 105 subcategories. Conclusions Due to the increased prevalence of Celiac disease and the lack of an established minimum set of data, determining the required educational information is of great importance at the national level. Such information could be useful in implementing educational health programs to raise the public level of awareness. In the field of education, such contents can be employed in planning new technology based on mobile phones (mobile health), preparing registries, and producing widely used content.

Small procedural changes in how regulatory agencies implement biotech policies can make significant differences in improving regulatory efficiency. This paper discusses how science based, crop specific guidance documents can improve dossier content and the review and approval of biotech varieties. In addition, we describe how the adoption of established risk assessment methodology and applying policy-linked decision making at the agency level can boost both efficiency and developer, public and government confidence in agency decision making and in biotech crops.

Background: Celiac disease is a major public health problem in many countries, including Iran. Considering the disease's exponential spread throughout the world and its risk factors, identifying the educational priorities and minimum data required to control and treat the disease is of great significance. Methods: The present study was conducted in two phases in 2022. In the first phase, a questionnaire was developed based on the information obtained from a review of the literature. Later, the questionnaire was administered to 12 pundits in the fields of nutrition (n = 5), internal medicine (n = 4), and gastroenterology (n = 3). As a result, the necessary and important educational content was determined for developing the Celiac Self-Care System. Results: According to the experts' viewpoints, the educational needs of patients were classified into nine categories of demographic information, clinical information, long-term complications, comorbidity, tests, medications, dietary recommendations, general recommendations, technical capabilities as well as 105 subcategories. Conclusions: Due to the increased prevalence of Celiac disease and the lack of an established minimum set of data, determining the required educational information is of great importance at the national level. Such information could be useful in implementing educational health programs to raise the public level of awareness. In the field of education, such contents can be employed in planning new technology based on mobile phones (mobile health), preparing registries, and producing widely used content. [ABSTRACT FROM AUTHOR]

Featured Application: Our machine learning-based label quality assurance demo showcases the potential of our approach to improve object detection projects within the data requirement stage of requirements engineering. The accompanying API enables easy integration with existing platforms. This approach reduces the resources needed for label quality assurance. In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance. [ABSTRACT FROM AUTHOR]

Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency is quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic. • Forecasting models for MPC tested using energy system simulation with historic data. • Impact of data on forecast accuracy and MPC operational performance quantified. • Simple linear MLP model provides equivalent accuracy to state-of-the-art models. • More than 2 years of training data did not significantly improve forecast accuracy. • Screening training data using change-points improves accuracy and data efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance.

The use of fish aggregating devices (FADs) in tropical tuna fisheries has increased significantly during recent decades. Concurrently, concern about juvenile tuna mortality, bycatch, and marine debris associated with FAD fisheries increased, and this led to the implementation of FAD management measures and more sustainable designs (e.g., non-entangling or biodegradable FADs, limits on active FADs, etc.). This document reviews data collection and reporting requirements of tuna-Regional Fisheries Management Organizations (t-RFMOs) on drifting FADs and summarizes the work carried out since 2010 under the Spanish FAD management plan to create an adequate standard data collection aimed at improving science-based decision making. The aim of this study is to assist in the strengthening of data collection systems through: (1) a review of the existing data requirements, (2) a review of the status of FAD data collection worldwide and identification of data gaps, and (3) recommendations aimed at improving FAD management through the strengthening of FAD data requirements. Due to the complexities of data collection, we summarize the difficulties faced when processing the data and propose concrete and practical solutions to improve both the data collection system and information quality. [ABSTRACT FROM AUTHOR]

Background and Aim: Preeclampsia is one of the most prominent cases of pregnancy related diseases that threatens health at global level, especially in developing countries. In Iran, with 14% of outbreak, it is the second most common cause of maternal mortality. The main goal of this study was to identify the information requirements of the Android-based preeclampsia self-Management application. Materials & Methods: This was a descriptive study that was done in 2018 in Amir_Almomenin Hospital affiliated to Semnan University of Medical Sciences in two stages of reviewing the sources and the need for data elements. In the review phase, after studying the articles and study, the data requirements and factors which affecting the prevalence of preeclampsia were identified and a survey of qualified physicians was done by designing a researcher-made questionnaire. Results: This research results indicate that 63.9% of the respondents assigned to the elements mentioned in the demographic findings. 75.9% of them identified health information elements as very important. Also, 77.85% of the research community considered the elements in the lifestyle sector to be of the highest importance. All participants recognized that reminder in the program was necessary. Approximately 33.33% of them reconsidered sport education to be at the lowest level, while 45.24% rated it as being of the highest importance. Conclusion: The information requirements of this program were determined in 6 groups of health history, educational tips, lifestyle, alarms, referral, and reporting. These programs can help pregnant mothers with preeclampsia to control their disease to minimize complications by observing proper nutrition and principles of treatment.

Kidney Exchange Programs (KEP) are valuable tools to increase the options of living donor kidney transplantation for patients with end-stage kidney disease with an immunologically incompatible live donor. Maximising the benefits of a KEP requires an information system to manage data and to optimise transplants. The data input specifications of the systems that relate to key information on blood group and Human Leukocyte Antigen (HLA) types and HLA antibodies are crucial in order to maximise the number of identified matched pairs while minimising the risk of match failures due to unanticipated positive crossmatches. Based on a survey of eight national and one transnational kidney exchange program, we discuss data requirements for running a KEP. We note large variations in the data recorded by different KEPs, reflecting varying medical practices. Furthermore, we describe how the information system supports decision making throughout these kidney exchange programs. [ABSTRACT FROM AUTHOR]

Genetically modified (GM) crops that have been engineered to express transgenes have been in commercial use since 1995 and are annually grown on 200 million hectares globally. These crops have provided documented benefits to food security, rural economies, and the environment, with no substantiated case of food, feed, or environmental harm attributable to cultivation or consumption. Despite this extensive history of advantages and safety, the level of regulatory scrutiny has continually increased, placing undue burdens on regulators, developers, and society, while reinforcing consumer distrust of the technology. CropLife International held a workshop at the 16th International Society of Biosafety Research (ISBR) Symposium to examine the scientific basis for modernizing global regulatory frameworks for GM crops. Participants represented a spectrum of global stakeholders, including academic researchers, GM crop developers, regulatory consultants, and regulators. Concurrently examining the considerations of food and feed safety, along with environmental safety, for GM crops, the workshop presented recommendations for a core set of data that should always be considered, and supplementary (i.e., conditional) data that would be warranted only on a case-by-case basis to address specific plausible hypotheses of harm. Then, using a case-study involving a hypothetical GM maize event expressing two familiar traits (insect protection and herbicide tolerance), participants were asked to consider these recommendations and discuss if any additional data might be warranted to support a science-based risk assessment or for regulatory decision-making. The discussions during the workshop highlighted that the set of data to address the food, feed, and environmental safety of the hypothetical GM maize, in relation to a conventional comparator, could be modernized compared to current global regulatory requirements. If these scientific approaches to modernize data packages for GM crop regulation were adopted globally, GM crops could be commercialized in a more timely manner, thereby enabling development of more diverse GM traits to benefit growers, consumers, and the environment., Competing Interests: Authors NS, JA, and CM were employed by Corteva™ Agriscience. Author AS was employed by CropLife International. Authors JS and MS were employed by BASF Corporation. Authors MH and SS were employed by Syngenta Seeds LLC. Authors DM and E-UW were employed by Bayer Crop Science. BASF Corporation, Bayer Crop Science, Corteva™ Agriscience, and Syngenta Seeds LLC are commercial developers of GM crops., (Copyright © 2024 Storer, Simmons, Sottosanto, Anderson, Huang, Mahadeo, Mathesius, Sanches da Rocha, Song and Urbanczyk-Wochniak.)

Workflow nets and their extensions with data flows, e.g., workflow nets with data and colored workflow nets, can well model the business processes of workflow systems, and their soundness guarantees that systems have neither deadlock nor livelock. However, some design requirements related to the changes of data values within multiple business cases are not reflected by these models, and thus, some errors of the modeled systems cannot be detected by them. In this article, we propose workflow nets with tables (WFT-nets) to model workflow systems that are closely related to the database (i.e., different users, records, and attributes). We define the firing rule of WFT-nets via data refinements in order to avoid infinite states caused by unbounded data. We use first-order linear temporal logic to represent the design requirements of records and attributes’ values and then define the soundness of WFT-nets. We construct an algorithm to verify soundness. A tool is developed, and a group of experiments are done to illustrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In the case of the African continent, the estimates of most climate forcing components are associated with large uncertainties, above all the greenhouse gas budget. The EU-funded SEACRIFOG project is designing an observation network which aims at reducing these uncertainties. In this practice paper, we present the various steps towards the design of this network and discuss the data-related implications. This includes the formulation of appropriate observational requirements for each variable considered essential to quantify Africa-wide climate forcing as well as an assessment of corresponding available observational infrastructures and data in order to determine data gaps, needs and priorities. The results are intended to inform the design of an interoperable African data infrastructure for environmental observations.

The REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) data requirements for mutagenicity and carcinogenicity have been compared to the criteria for classification under Classification, Labelling and Packaging (CLP) and the studies used as key evidence by Committee for Risk Assessment (RAC) in drafting its opinions on the appropriate classification. This comparison revealed that the REACH information requirements will not provide sufficient information to conclude a substance is a Cat 1B mutagen and/or carcinogen. In addition, requiring such information via a substance evaluation under REACH requires a large investment from the Member States and takes years. Classification and labeling is essential in the communication of the hazardous properties of substances and mixtures and is amongst others an important first step in the identification of a substance as a Substance of Very High Concern (SVHC). REACH will hardly generate sufficient information for classification of substances as category 1B for mutagenicity and carcinogenicity. Therefore, indications of very severe hazards of substances are missed and health risks could occur. There are various ways to deal with this problem, however as most of these require adaptation of regulations this will cost considerable time and political will. This study is a first step to raise awareness for the problem and to start a discussion to search for a sustainable solution. [ABSTRACT FROM AUTHOR]

Highlights • A review taxonomy for human-in-the-loop HVAC operations has been proposed. • Human-in-the-loop HVAC operations have been reviewed using the proposed taxonomy. • Methods for occupancy and comfort characterization were systematically reviewed. • Methods for integration of human dynamics in the control of HAVC were reviewed. • Presented quantitative and qualitative performance assessment on different methods. Abstract Heating, ventilation, and air-conditioning (HVAC) systems account for almost half of the energy consumption in buildings. By benefiting from advancements in information and communication technology, human-in-the-loop HVAC operations have drawn considerable attention in the last decade with the aim of curtailing unnecessary energy use and providing user-specific comfort zones with reduced user dedication. Future progress in thie field calls for an in-depth understanding of the current state and challenges of the human-in-the-loop HVAC systems. Therefore, using a structured literature review approach, we have investigated this field according to two parameters of human dynamics that drive user-centric operations of HVAC systems, namely, occupancy and comfort. In this review and assessment study, by proposing a five-tier hierarchical taxonomy, we have classified the studies based on their contributions to occupancy- and comfort-driven human-in-the-loop HVAC operations (e.g., occupancy detection or comfort profiling) and have presented categorization for techniques and their quantitative performance assessment. In doing so, we have accounted for the context of the studies as they relate to developments in residential and office buildings given that distinct circumstances in each context (e.g., accessibility to thermostats) have resulted in different methodologies, especially in adopting the sensing techniques and HVAC operations. Moreover, we have distinguished simulations from field evaluations to assess the actual viability and challenges in achieving desirable results in practice. Lastly, the Hype cycle model was utilized to qualitatively evaluate the developments of different technologies for human-in-the-loop HVAC operations from a research perspective. [ABSTRACT FROM AUTHOR]

Background and Aim: Preeclampsia is one of the most prominent cases of pregnancy related diseases that threatens health at global level, especially in developing countries. In Iran, with 14% of outbreak, it is the second most common cause of maternal mortality. The main goal of this study was to identify the information requirements of the Android-based preeclampsia self-Management application. Materials & Methods: This was a descriptive study that was done in 2018 in Amir_Almomenin Hospital affiliated to Semnan University of Medical Sciences in two stages of reviewing the sources and the need for data elements. In the review phase, after studying the articles and study, the data requirements and factors which affecting the prevalence of preeclampsia were identified and a survey of qualified physicians was done by designing a researcher-made questionnaire. Results: This research results indicate that 63.9% of the respondents assigned to the elements mentioned in the demographic findings. 75.9% of them identified health information elements as very important. Also, 77.85% of the research community considered the elements in the lifestyle sector to be of the highest importance. All participants recognized that reminder in the program was necessary. Approximately 33.33% of them reconsidered sport education to be at the lowest level, while 45.24% rated it as being of the highest importance. Conclusion: The information requirements of this program were determined in 6 groups of health history, educational tips, lifestyle, referral alarms, and reporting. This program can help pregnant mothers with preeclampsia to control their disease to minimize complications by observing proper nutrition and principles of treatment. [ABSTRACT FROM AUTHOR]

Hazard classification and risk assessment of substances, is essential to protect workers and consumers from hazardous substances including reproductive toxicants. The ability to classify substances for reproductive toxicity under the current REACH information requirements has been assessed. For low tonnage substances (<10 ton per annum (tpa)) information for classification is insufficient. When only a reproductive screening study is available (10–100 tpa), substances are mostly not classified in Category 1B as developmental and non-potent fertility effects may be missed. The information requirements could be improved by automatic triggering of follow-up studies in case of a Category 2 classification based on a screening study. Additionally, a study could be added to the information requirements for substances produced at 1–10 tpa. Performing a risk assessment is often problematic due to the limited study requirements at low tonnage levels. Only for substances produced at more than 100 tpa, there is a high likelihood to detect reproductive effects and perform accurate risk assessment provided that the extended-one-generation-reproductive-toxicity-study and/or extra cohorts are triggered where required. Regardless of the tonnage level, no specific studies on lactation are required. With this paper we intend to contribute to the discussion on the information requirements for reproductive toxicity in view of the REACH revision. • At Annex X, REACH provides sufficient information regarding fertility and development endpoints. • At Annex IX, information on fertility and development will be sufficient provided the studies are triggered where required. • Under-classification and underestimation of the risk are problems for low tonnage substances (Annex VIII and lower). • REACH does not provide information on effects on lactation at any tonnage band. • Extending the REACH information requirements is needed to better identify substances toxic to reproduction. [ABSTRACT FROM AUTHOR]

Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.

Aiming at the problem of the diversity of multi-disciplinary simulation data of integrated circuits and decentralized data storage, the data requirements of integrated circuit simulation project were summarized, including signal integrity simulation, thermal simulation, vibration simulation and life simulation project. The simulation system based on C/S architecture was used to manage and process the collaborative simulation data, which can realize the standardized storage of the co-simulation data and the comparison of the relevant simulation performance parameters, and support data interface with ANSYS software and SIGRITY software, which can directly read the integrated circuit simulation output of the numerical file. The results show that the software system has the functions of collaborative simulation data management and data processing, and support data interface with a variety of CAE tools, which provides a guarantee for the sharing and delivery of integrated circuit co-simulation data. [ABSTRACT FROM AUTHOR]

Firefighters require accurate and timely information regarding a building and its environment to perform their duty safely and successfully during a fire emergency. However, due to the chaotic nature of building fires, firefighters often receive erroneous, conflicting, or delayed information that can affect the outcome of an emergency. In this paper, we propose a solution in the form of an ontology that defines building and environmental data that is needed by firefighters during a building fire emergency. The ontology development followed the METHONTOLOGY method and was implemented using the web ontology language (OWL) in Protégé 5.5.0. Built-in reasoners in Protégé and an ontology pitfall scanning tool were employed to verify the structure and consistency of the new ontology. To validate the ontology efficacy, we developed a prototype web application to represent and visualise relevant information based on the ontology and used that as a basis for conducting interviews with firefighters. Finally, a specification document that describes the ontology was created and published online. The proposed ontology can be a basis for developing intelligent tools and systems that support firefighters. [ABSTRACT FROM AUTHOR]

A conceptual approach is proposed to define 3D geo-information requirement for different types of disasters. This approach includes components such as Disaster Type-Sector-Actor-Process-Activity-Task-Data. According to disaster types processes, activities, tasks, sectors, and responsible and operational actors are derived. Based on the tasks, the needed level of detail for 3D geo-information model is determined. The levels of detail are compliant with the 3D international standard CityGML. After a brief introduction on the disaster phases and geo-information requirement for actors to perform the tasks, the paper discusses the current situation of disaster and emergency management in Turkey and elaborates on components of conceptual approach. This paper discusses the 3D geo-information requirements for the tasks to be used in the framework of 3D geo-information model for Disaster and Emergency Management System in Turkey. The framework is demonstrated for an industrial fire case in Turkey. [ABSTRACT FROM AUTHOR]

TheHighway Safety Manual(HSM) assists state and local agencies in improving highway safety by moving toward statistically proven quantitative analyses. The manual recommends using Empirical Bayes (EB) method with locally derived calibration factors to predict the agency's safety performance. It recommends deriving calibration factors using randomly selected 30 to 50 roadway sites that experienced a minimum of 100 crashes per year. Given the fact that the minimum sample size is a function of sample variance, this recommendation is clearly questionable as roadway characteristics of different roadway types are likely to have different levels of homogeneity. This research used Florida data to determine the minimum sample sizes to estimate reliable calibration factors for the following three facility types: rural two-lane roads, rural multilane highways, and urban and suburban arterials. The analysis was based on the data collected from more than 7,000 miles of segments and more than 1,000 intersections in Florida. The minimum sample size was determined such that there is a high probability that the calibration factor estimated from a sample is within 5% to 10% of the actual calibration factor calculated from the entire data set. For all the site subtypes, it was found that the minimum sample size of 30 to 50 sites, as recommended by the HSM, is insufficient to achieve the desired accuracy. Moreover, the sample sizes required for estimated calibration factors to lie within 5% of the actual calibration factors is almost double the sample sizes required for estimated calibration factors to lie within 10% of the actual calibration factors. The results also showed that the generalized one-size-fits-all approach of using a sample size of 30 to 50 sites is not appropriate as different facility types require different sample sizes depending on several factors, such as the extent of data variability, population size, crash experience, and so on, to estimate reliable calibration factors. [ABSTRACT FROM PUBLISHER]

Adjuvants have been incorporated into vaccines for decades to improve the immune response to vaccine antigens. In developing new vaccines or simply improving existing vaccines, interest in adjuvants has been growing rapidly, with various types of adjuvant (some novel; some already incorporated into specific vaccines) used in clinical research or under development. The development of adjuvants is generally closely linked to the development of a specific vaccine. With the adjuvant being an integral constituent of the final medicinal product, current legislation does not foresee authorization being granted for an adjuvant as a stand-alone product. A dedicated EU guideline addresses the quality non-clinical and clinical development of vaccine adjuvants, and this guidance needs to be considered alongside the specific guidelines for medicinal products in general. This paper provides an overview of the requirements for developing or modifying an adjuvanted vaccine. The authors also address the experience gained for the adjuvanted 2009 H1N1 pandemic influenza vaccine and its implications. [ABSTRACT FROM AUTHOR]

According to the International Plant Protection Convention and the World Trade Organization Agreement on the Application of Sanitary and Phytosanitary measures, any measure against the introduction and spread of new pests must be justified by a science-based pest risk analysis. Economic impact assessments are usually carried out using a qualitative approach, based on classifying the size of impact into five categories, from “minimal” to “massive”. Whilst the qualitative approach may be adequate in many instances, it lacks transparency and demonstrable objectivity. A quantitative approach for economic impact assessment may improve transparency and strengthen the justification for measures, if taken, but requires additional work, and it requires specific data and models. This paper, first, compares the strengths and weaknesses of qualitative and quantitative approaches. Second, it clarifies the data and models needed to conduct a quantitative economic impact assessment to support a decision on the pest quarantine status or justify management measures. Third, it identifies the criteria for choosing the appropriate level of complexity, regarding the resolution, economic technique and time frame of the quantitative approach. The greater transparency and objectivity of the quantitative vis-a-vis qualitative economic impact assessment may enhance plant health policy and decision making at national and international regulatory bodies. However, uncertainties that are inherent to this approach may weaken this position. Hence, PRAs require a mixture of quantitative and qualitative approaches for assessing impacts and the exact balance of the two has to be case-specific. [ABSTRACT FROM AUTHOR]