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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]

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.)

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.

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]

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.

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]

Model predictive control (MPC) has shown potential in improving building performance but is bottlenecked by the difficulty in constructing control-oriented models. The challenge lies in evaluating the sufficiency of the model and the data usage beforehand. This paper bridges the knowledge gaps in the interactions between data requirements, model quality, and control performance by integrating real-world measurements and simulation-based experiments. The data usage related to occupancy and Internal heat gain (IHG) was studied considering its importance and the absence of consensus in the literature. Varying occupant-related data sources were tested as RC model inputs, including none, schedule, electricity consumption, CO 2 ppm, and ideal measurement. Combinations of model inputs and complexities were examined for prediction and control in an office, a classroom, and multi-zone offices on one floor. The results indicated that the usefulness of data is jointly affected by three factors: measurement suitability, model complexity, and modeling purpose. Given the adequate model structure, satisfying prediction and control performance was achieved in offices with no detailed measurement. Meanwhile, electricity and CO 2 were needed together to capture the IHG influence and realize the good performance for classrooms. The experiments also uncovered the heterogeneous requirements on models from traditional prediction tests and the control tasks. Lower prediction error did not always mean better control. More importantly, we provided the first quantitative demonstration of the complementary relationship between model adequacy and data informativeness with respect to different purposes. This study advocates the pioneering idea of sparse data usage and parsimonious modeling, which promotes the actual application of MPC in buildings by guiding control-oriented model development. [Display omitted] • Over 10% energy saving and acceptable PMV achieved without occupant-related data. • Occupant-related data is needed only if the internal heat gain has high variability. • Adequate model with sufficient data yields physically representative identification. • Better physical representation could lead to larger RMSE but more precise control. • Sparse data and parsimonious modeling should be pursued in real-world implementation. [ABSTRACT FROM AUTHOR]

The increasingly stringent regulatory regime governing the registration of active substances and plant protection products in the European Union (EU) is deterring R&D companies from introducing products based on new active substances into the market. They are instead developing new products for initial market introduction in other parts of the world. Consequently EU farmers and growers are being denied products that are available elsewhere. The cost of maintaining existing active substances is also becoming prohibitive, leading to loss of products from the market with the consequential impact on growers' ability to produce many crops. Minor uses are suffering in particular. This in turn is having an impact on food production and food security within the EU. The European Commission is urged to take advantage of the opportunity available in the current legislation to make proposals for improvements and amendments in order to redress the imbalance between the EU and other markets. [ABSTRACT FROM AUTHOR]

UrbanSim has significant data requirements. In particular, it requires disaggregate data (traditionally at the 150 meter by 150 meter gridcell level) for employment, households, and buildings. While such data are not always easily available, most regions have readily available data in a more aggregate form, often at the level of traffic analysis zone (TAZ) or other municipal divisions. This paper describes two UrbanSim applications for the cities of Brussels, Belgium and Lyon, France that adopted different approaches of using aggregate data. In Brussels, aggregate zonal data were disaggregated to the gridcell level. In the Lyon application, the zone was used as the unit of analysis and as such, each zone corresponds to one gridcell. The objectives of this paper are: 1) establish whether an UrbanSim model can be developed using aggregate data; 2) describe two different approaches to using aggregate data with UrbanSim and evaluate; and 3) evaluate the advantages and disadvantages of using aggregate data, as well as the two different approaches described. In doing so, it advances knowledge in the field of transportation and land use modeling by helping modelers evaluate the use of an increasingly popular integrated transportation land use modeling option. Several conclusions flow from this work. First, aggregate data can be used to develop UrbanSim models. Second, only a limited amount of disaggregate information can be drawn from aggregate data. In the context of UrbanSim, this is manifested in models with relatively few variables and dubious simulation results—in other words, while it is possible to develop an UrbanSim application with aggregate data, it should not be used for applied analysis. Finally, the development of such models can be a relatively low-cost exercise to gain familiarity with UrbanSim’s functioning and data requirements. As a result, it can also be seen as an important first step to developing or evaluating UrbanSim for application in a new region.

Simmonds, E. J., Döring, R., Daniel, P., and Angot, V. 2011. The role of fisheries data in the development evaluation and impact assessment in support of European fisheries plans. – ICES Journal of Marine Science, 68: 1689–1698.The European Commission has two formal requirements for developing multi-annual management plan policies: evaluation of the past performance of existing plans and impact assessments (IAs) of the potential benefits of new proposals. The new policies require the evaluation of fishery management in terms of three specific criteria: (i) effectiveness, i.e. the best method to achieve the objectives; (ii) efficiency, the cost-effectiveness, and proportionality; and (iii) consistency, i.e. limiting trade-offs across economic, social, and environmental domains. To develop policy, there is a need to collect relevant information, then to conduct appropriate analyses that provide documentation to support the policy objectives. This paper discusses the data requirements for good environmental, economic, and social understanding of fishery dynamics and management, describing how fishery data are currently linked to the analytical and management evaluation process using examples from 2009 and 2010. The type of information currently used is considered, along with the timetable of data availability, and its effect on historical evaluation and IAs, which are now formally required when any changes to legislation are proposed in the European Union, including following stakeholder consultation. The possibilities and future needs for such data are discussed. [ABSTRACT FROM PUBLISHER]

Conservation biogeography involves the application of biogeographical principles, theories, and analyses to problems regarding biodiversity conservation. The field was formally defined in 2005, and considerable research has been conducted in the ensuing 5 years. This editorial sets the context for 16 contributions in a special issue of Diversity and Distributions on developments and challenges in conservation biogeography. Papers are grouped into the following main themes: species distribution modelling; data requirements; approaches for assigning conservation priorities; approaches for integrating information from numerous disparate sources; special challenges involving invasive species; and the crucial issue of determining how elements of biodiversity are likely to respond to rapid climate change. One paper provides a synthesis of requirements for a robust conservation biogeography for freshwater ecosystems. Conservation biogeography is well poised to make a significant contribution to the process of providing policy makers with objectively formulated scenarios and options for the effective management of biodiversity. The editorial, and the papers in the special issue, deliberate on many of the exciting developments in play in the field, and the many complex challenges that lie ahead. [ABSTRACT FROM AUTHOR]

Abstract: Ecosystem functions are a central topic of environmental valuation research. Lay respondents are usually unfamiliar with the implications of scientific descriptions of ecosystem functioning. Thus, the applicability of stated preference methods for the valuation of ecosystem functions is a matter of debate. In the general discourse on the economic valuation of ecosystem functions, it was suggested to valuate ecosystem functions via the ecosystem services they provide. In this contribution, we argue that the recognition of this principle is the key also for the applicability of stated preference methods to the valuation of ecosystem functions. Successful application requires a precise differentiation between the descriptive realm of ecosystem functions and the evaluative realm of ecosystem services. On this basis, an ecosystem service approach for the economic valuation of ecosystem functions is introduced. We illustrate the ecosystem service approach by the valuation of a hydrological ecosystem function in rural Indonesia. Identification and representation of the ecosystem services were based on extensive investigations of respondent perception of hydrological phenomena. The availability of irrigation water during the dry season turned out to be a locally decisive ecosystem service. Within the case study, willingness-to-pay (WTP) values were estimated for changes in the availability of irrigation water, rattan, shading in cacao plantations, and in the population of an endemic mammal by Nested Logit (NL) analysis of choice experiment data. Only few respondents lacked sufficient familiarity of the environmental goods to be valued. WTP for a reduction in water scarcity by one month was ~39–40,000 Indonesian Rupiah/household/year. A rating of respondent comprehension correlates with preferences for water availability. For respondents with above average comprehension (rated 4 versus mean of 3.12 at a 5-point scale), NL analysis predicts a 14.1% increase in WTP. Interactions of attitudinal constructs support the notion that the WTP estimate is an expression of underlying values and risk perceptions. The ecosystem service approach comes at a cost. Because ecosystem services relevant to local respondents are valued rather than scientifically described ecosystem functions, typical ‘basic science’ models that represent ecosystem functioning cannot be used for the analysis of valuation scenarios with direct policy relevance. Engineering-type models that embody technical and, in our case study, agronomic knowledge are necessary to bridge the gap between ecosystem functioning and their practical implications. A lack of this kind of information also hinders a meaningful application of alternative valuation approaches. [Copyright &y& Elsevier]

Justification for efficacy testing is provided under the present FIFRA, and the PR notice 70-15 requirements. In addition, the Pure Food and Drug Laws, the Delaney Amendment, and other laws effect the requirements of registration of all economic poisons. Basic preliminary registration information such as toxicological data, chemistry data, must be provided on all chemicals proposed as economic poisons. Once the basic chemical and toxicological properties have been determined, the applicant must consider basic efficacy requirements. Efficacy requirements should consider the effects of particle size and shape, taste and odor, impurities, diluents, stickers and solvents, volatility, mode of action, and other factors such as age, sex, species, characteristics and ambient temperatures. Specific studies, however, will vary with the intended use of the product and the target species involved. Field testing is required for all proposed products under actual field situations. These tests logically follow appropriate laboratory tests. The risk-benefit ratio is defined as a ratio of hazards to nontarget organisms as compared to the benefits resulting from the product’s use. At present, this ratio has not been made a part of the registration procedure but has been used in adverse action.

Background: Research conducted on mobile applications monitoring mental health has concluded that patients with mental disorders tend to use such applications to maintain mental health balance and applying technology may help manage and monitor issues like bipolar disorder. Objective: This study has been done to identify the necessities of designing a mobile application for bipolar disorder affected patients. Material and Methods: 1) Literature search, 2) analyzing existing mobile applications to examine their efficiency, 3) Interviewing patients with bipolar disorder to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Results: Literature search and mobile application analysis resulted in 45 characteristics, which was later reduced to 30 after the experts were surveyed about the project. The characteristics included the following: mood monitoring, sleep schedule, energy level evaluation, irritability, speech level, communication, sexual activity, self-confidence level, suicidal thoughts, guilt, concentration level, aggressiveness, anxiety, appetite, smoking or drug-abuse, blood pressure, the patient's weight and the side effects of medication, reminders, mood data scales, diagrams or charts of the collected data, referring the collected data to a psychologist, educational information, sending feedbacks to patients using the application and standard tests for mood assessment. Considering the first phase analysis along with expert and patient views, the most and least important matters among them are mood tracking and a list of psychiatrists respectively. Conclusion: The present study has been conducted by identifying the necessities of applications intended for managing and monitoring bipolar patients to maximize efficiency and to subsequently minimize the relapse and side effects. [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. [ABSTRACT FROM AUTHOR]