A Decision Support Tool for the Optimal Monitoring of the Microclimate Environments of Connected Smart Greenhouses

Development of an applicability and performance evaluation tool based on energy simulation for smart greenhouse cooling packages

The influence of cognitive science and psychology on decision theory is bringing about changes to assumptions about decision making, and, as a consequence, the way that decisions should be modeled and supported. The three articles published in this Special Issue on intelligent decision support and modeling reflect an emerging literature that incorporates psychology into decision modeling and decision support. This literature represents only a starting point, and much remains to be done in terms of acknowledging the influence of psychology on engineering decisions. Decision support tools will probably never completely make up for engineers’ lack of the cognitive capacity needed to make the multitude of decisions associated with engineering design in a fully informed, unbiased way. Incorporating rational choice theory and psychology into decision support tools seems to be a fruitful path toward promoting optimal decisions in engineering. The last two to three decades have brought about important changes to the field of decision theory, particularly in response to the generally accepted principle that the act of cognitive representation and framing of decisions should follow the axioms of expected utility theory. The standard theory for decision making is based on subjective expected utility theory (e.g., Savage, 1954; Schmeidler, 1989), which involves the enumeration of possibilities, an analysis of the possible outcomes, and the selection of the utility-maximizing decision (Gilboa & Schmeidler, 2001). In engineering design, decision theory is generally applied as a systematic procedure for selecting design variables when there is uncertainty over the preferences associated with the objectives (Thurston, 1991, 2001). In schools of engineering, operations research, computer science, and business around the world, students continue to be taught a set of methodologies consonant with rational choice theory (Wood, 2004), which is founded upon the analysis of information as the basis of decision. In short, decision support and modeling in engineering design generally follows a framework of selecting design variables that optimize the expected utility of the design, and, in so doing, casts engineering design within a rational process (Hazelrigg, 1998). The perceived increased accuracy and rigor of the decisions and the decision support systems built upon the premise of utility maximization can mask the realities of eliciting preferences from engineers, which may be subject to psychological biases. Kahneman and Tversky (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979) drew attention to the realities of human decision making in describing the heuristics that human beings employ in decision making under uncertainty, which are subject to psychological biases that can lead to systematic and predictable errors. In recent years, Kahneman and colleagues have published a series of articles dealing with strategies to correct for these psychological biases (e.g., Kahneman & Lovallo, 1993; Kahneman et al., 2011) and entire fields of behavioral finance, behavioral economics, and behavioral strategy have grown up around the application of cognitive science and psychology to the theory and practice of decision making under uncertainty in specific contexts. This influence is now being felt in the engineering design domain in the modeling and support of engineering decisions, which is built upon a rich heritage of studies on the cognitive and behavioral strategies of engineers. The three articles published in this Special Issue reflect a shift away from normative subjective utility maximization as the only model for decision making and decision support toward greater consideration to the exigencies of engineering decision making in practice. All three articles contribute to decision support and modeling and treat these as integrated issues rather than as compartmentalized problems. The article “Bayesian Project Diagnosis for the Construction Design Process” by Matthews and Philip is perhaps the most “traditional” of the three articles. The article deals with the problem of forecasting potential problems in construction processes. We use traditional in the sense that Bayesian modeling is an accepted method for estimating the probability of an event or outcome of interest (Marshall & Oliver, 1995). They model the construction process as a Markov chain, with transition probabilities associated with progressing through various Reprint requests to: Andy Dong, Faculty of Engineering and Information Technologies, University of Sydney, Engineering Building (J05), Sydney 2006, Australia. E-mail: andy.dong@sydney.edu.au Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2012), 26, 371–373. # Cambridge University Press 2012 0890-0604/12 $25.00 doi:10.1017/S0890060412000248

BackgroundClean birth practices can prevent sepsis, one of the leading causes of both maternal and newborn mortality. Evidence suggests that clean birth kits (CBKs), as part of package that includes education, are associated with a reduction in newborn mortality, omphalitis, and puerperal sepsis. However, questions remain about how best to approach the introduction of CBKs in country. We set out to develop a practical decision support tool for programme managers of public health systems who are considering the potential role of CBKs in their strategy for care at birth.MethodsDevelopment and testing of the decision support tool was a three-stage process involving an international expert group and country level testing. Stage 1, the development of the tool was undertaken by the Birth Kit Working Group and involved a review of the evidence, a consensus meeting, drafting of the proposed tool and expert review. In Stage 2 the tool was tested with users through interviews (9) and a focus group, with federal and provincial level decision makers in Pakistan. In Stage 3 the findings from the country level testing were reviewed by the expert group.ResultsThe decision support tool comprised three separate algorithms to guide the policy maker or programme manager through the specific steps required in making the country level decision about whether to use CBKs. The algorithms were supported by a series of questions (that could be administered by interview, focus group or questionnaire) to help the decision maker identify the information needed. The country level testing revealed that the decision support tool was easy to follow and helpful in making decisions about the potential role of CBKs. Minor modifications were made and the final algorithms are presented.ConclusionTesting of the tool with users in Pakistan suggests that the tool facilitates discussion and aids decision making. However, testing in other countries is needed to determine whether these results can be replicated and to identify how the tool can be adapted to meet country specific needs.

Aerial application of dispersants are an effective means of responding to oil spills in coastal waters and the deeper waters of the Outer Continental Shelf or Gulf of Mexico. To ensure the safety of responders and nearby wildlife, a buffer area is put in place around the spilled oil to be treated, within which spraying operations are conducted. In 2015, a research project was initiated to develop a prototype Decision Support Tool (DST) designed specifically for estimating the spray drift during the aerial application of dispersants on an oil spill. In 2019, an initiative was undertaken to further develop the DST and address known data gaps in the modeling used in the prototype, expand on the aircraft included in the tool, and include a contour plot output of dispersant deposition. The DST has been designed specifically for estimating the spray drift during the aerial application of dispersants on an oil spill through the use of complex Computational Fluid Dynamics (CFD) modeling. The DST program operational space was developed based on direct input from Oil Spill Response Operators (OSROs) for ten airframes currently used in the United States for aerial response operations, including both turbo propeller and turbo fan engine types. The DST employs a database of results generated using the latest in CFD modeling technology to examine flow structures and drift effects created by various operating conditions, coupled with specific configurations of different oil spill response aircraft and their spray systems (boom and nozzle configurations). The DST uses a Response Surface Curve (RSC) for each airframe to predict the drift extent of dispersant particles and mass deposition concentration, the RSC for each airframe was derived from a database of results generated using the latest CFD modeling technology. The studies conducted to generate data for the DST RSCs provided considerable insight into the relationships between the particle dispersant behavior for different airframe types. Trends were identified in particle dispersion behavior when airframes were flown with a heavy payload (full weight) compared to lighter payload (empty weight). These trends change depending on the airframe used and, more specifically, the location and arrangement of the boom used to release the droplets relative to the location of the main wing. Change in Particle Size Distribution (PSD) was also investigated for flight operations of one airframe and the impact on the drift extent reported. The DST will provide oil spill responders with information related to the extent of any areas potentially impacted by dispersant drift. This will assist the operational control personnel in establishing setback distances, information which becomes increasingly important as a spill escalates beyond a Tier 1 response where the size of the spill, and the resources committed, become significant. In addition, the DST generates a contour plot of mass deposition at ground level based on the operational and environmental parameters input to the program, providing the user with a graphical display of where the majority of the aerial dispersant is predicted to land. While the analysis and tool development are complete, a formal peer review has not been completed at the time of the paper publication.

The study addresses the crucial topic of designing and implementing smart systems in agricultural production, focusing on the development of a "Smart Greenhouse" utilizing neural networks. It thoroughly examines key technological innovations and their role in sustainable agriculture, emphasizing the collection, processing, and analysis of data to enhance plant growth conditions. The research highlights the efficiency of resource use, management of humidity, temperature, carbon dioxide levels, and lighting, as well as the automation of irrigation and fertilization. Special attention is given to developing adaptive algorithms for predicting optimal conditions that increase crop yield and quality while reducing environmental impact and costs. This opens new avenues for the sustainable development of the agricultural sector, promoting more efficient and environmentally friendly farming practices. Utilizing a literature review, comparative analysis of existing solutions, and neural network simulations for predicting optimal growing conditions, the study makes a significant contribution to applying artificial intelligence for greenhouse microclimate management. It explores the potential of AI in predicting and optimizing growing conditions, potentially leading to revolutionary changes in agriculture. The research identifies scientific innovations, including the development and testing of predictive algorithms that adapt to changing external conditions, maximizing productivity with minimal resource expenditure. The findings emphasize the importance of further studying and implementing smart systems in agriculture, highlighting their potential to increase yield and improve product quality while reducing environmental impact. In conclusion, the article assesses the prospects of neural networks in the agricultural sector and explores possible directions for the further development of "Smart Greenhouses".

To overcome climate change that affects agriculture in all over the world, shows the estate and farming for the raising of the extreme weather (e.g., floods, droughts, etc). Therefore, the smart greenhouse is essential in providing sustainable food for humans. The application of Smart Greenhouse, The ultimate and most common monitored variables are temperature, moisture, the intensity of sunlight and environmental conditions to support the using of sunlight a source of electricity. This is necessary for the Smart Greenhouse to manipulates the environment in appropriate to its plant’s needs. In this case, the temperature and humidity in the greenhouse are important to keeps the productivity of the plants. So, it’s using automatic system to keep suitable environmental conditions as explained before for the plants. In this research, the smart Greenhouses is shaped Ferris wheel to make sprinkling and radiation of sunlight for plants is equally distributed for all plants and its parts, Based on the results of this experiments with rotary mechanical system for Smart Greenhouse as well shown that Smart Greenhouse can preserve suitable temperature and moisture for growing the plants.

Human Factors concerns must be integrated in the design and development of automation to assist air traffic management efficiency and safety in future traffic systems. This paper describes the practices used in development of an advanced decision support tool (DST) for addressing and resolving the human performance and information processing requirements. The en route descent advisor (EDA) is an advanced DST that assists controllers with metering of arrival aircraft in transition from Center to TRACON airspace. EDA generates comprehensive control advisories, allowing for efficient traffic control and compliance with metering and separation requirements. In today's environment controllers must rely entirely on their skill and judgment to provide instructions for conformance to flow rate restrictions and conflict avoidance. Automation that allows accurate, efficient ground-based control of the transition and descent phase of flight will result in a reduction in workload, flight deviations, and fuel consumption. A fundamental criterion for successful development of an advanced DST must be the acceptance and trust in the tool for use in an operational environment. The DST must provide a functional reduction in the controller's workload during periods of intense traffic demands, as well as during normal flow conditions. For use in traffic control, the DST must be accurate, stable, and efficient in all possible applications. Achieving these fundamental human factors design goals requires a dedicated focus on development of robust and versatile algorithms and the underlying processes that support the DST functions. Since EDA is still in the concept development phase, the project presents a unique opportunity for early and continuous human factors involvement throughout the development and evaluation cycle. To address these human factors concerns, simulation, testing and validation are performed as an integral part of the overall EDA development activity. The incremental process for EDA development permits controller evaluations and recommendations to be included in the development of the mature DST capabilities. This paper discusses controller-in-the-loop trials for development of trajectory visualization and other information presentations.

One of several common water conserving techniques is on-site stormwater harvesting for non- drinking purposes. However there is a lack of knowledge on the actual cost-effectiveness and performance optimisation of any stormwater harvesting system. At present stormwater harvesting systems are proposed and installed without any in-depth analysis of its effectiveness in various climate conditions. In particular the proposed design storage volume could be overestimated or underestimated. The biggest limitation of stormwater harvesting schemes is the rainfall variability, which will control the size of the storage needed and can't be based on long-term average annual rainfall data. A stormwater harvesting system designed considering average annual rainfall will not provide much benefit for a critical dry period. Similarly, a stormwater harvesting design for a particular region will not be similar for stormwater harvesting design in other regions. With all these uncertainties, even with several awareness campaigns and financial incentives, there is a general reluctance to adopt any potential stormwater harvesting measure. The main reasons behind this are that people are not aware of the payback period for their initial investment and the optimum size of the storage required satisfying their performance requirements. It is necessary to quantify the expected amount of water that can be saved and used through any particular harvesting technique based on contributing catchment size, tank volume, geographic location, weather conditions and water demand. Without proper analysis and quantification, any adopted tank size may not be cost-effective. This paper presents development of a comprehensive decision support tool to analyse and optimize a potential rainwater tank. The tool was developed based on daily water balance analysis, incorporating daily rainfall, runoff generated from roof after losses, daily water demand, tank size and overflow from the tank. For insufficient/no rainwater, the analysis assumes augmented supply from townwater supply. The developed tool enables a simple quantitative analysis of the expected water that can be saved based on the relevant constraints. The input data require are: daily rainfall, roof area, expected loss from roof to tank, tank volume and daily rainwater demand. The tool produces graphs showing cumulative yearly rainwater used, overflow and augmented townwater supply. To account for climate variability, provision has been made in the tool to analyse for a particular option in three different years (climate conditions), for which often a dry year, an average year and a wet year are considered. Also, the tool enables a life cycle costing analysis and payback period of any particular proposed tank size through the simulated outputs related to expected water savings per year, initial construction costs and operational costs related to tank. Expected water savings are calculated by tanking average of three (dry, average and wet) separate year's cumulative annual water savings. For the cost analysis, additional input data require are: water price, water price increment rate and maintenance cost increment rate. Also, the tool calculates the reliability of a particular size tank, connected with a particular roof size to fulfill the expected rainwater demand. Reliability is a measure of percentage of days in a year, when the tank was able to supply the expected demand. The paper illustrates different scenario results produced by the tool for daily rainfall data near Melbourne City, under different climatic conditions. The simulated results were compared with an earlier published spreadsheet based model results. The developed tool and the earlier spreadsheet based model produce exactly same results. The developed tool is a user- friendly tool which will make end-users decision making process easy, effective and knowledgeable.

Household behavior related to water conservation

Current status, advancements and development needs of geospatial decision support tools for marine spatial planning in European seas

Understanding how stakeholders manage risks associated with nanomaterials is a key input to the design of strategies and tools to achieve safe and sustainable nanomanufacturing. The paper presents some results of a study aiming firstly to inform the development of a software decision support tool. Further, we seek also to understand existing tools used by stakeholders as a source of capabilities and potential adaptation into decision support framework and tools. Central research questions of this study are: How is collective decision-making on risk management and sustainable nanomaterials organised? Which aspects are taken into account in this collective decision-making? And what role can a decision support tool play in such decision-making? The paper analyses 13 responses to a questionnaire survey held among participants in a meeting in October 2013 and a series of 27 semi-structured telephone interviews conducted from January until April 2014 with decision-makers from mainly European industry and regulators involved in risk management and sustainable manufacturing of nanomaterials. Findings from the study on the social organisation of collective decision-making, aspects taken into account in decisions and potential role of decision support tools are presented.

Clinical Decision Support Tools

Rehabilitation of the building envelope of hospitals: Achievable energy savings and microclimatic control on varying the HVAC systems in Mediterranean climates

Decision-support tools (DSTs) synthesize complex information to assist environmental managers in the decision-making process. Here, we review DSTs applied in the Baltic Sea area, to investigate how well the ecosystem approach is reflected in them, how different environmental problems are covered, and how well the tools meet the needs of the end users. The DSTs were evaluated based on (i) a set of performance criteria, (ii) information on end user preferences, (iii) how end users had been involved in tool development, and (iv) what experiences developers/hosts had on the use of the tools. We found that DSTs frequently addressed management needs related to eutrophication, biodiversity loss, or contaminant pollution. The majority of the DSTs addressed human activities, their pressures, or environmental status changes, but they seldom provided solutions for a complete ecosystem approach. In general, the DSTs were scientifically documented and transparent, but confidence in the outputs was poorly communicated. End user preferences were, apart from the shortcomings in communicating uncertainty, well accounted for in the DSTs. Although end users were commonly consulted during the DST development phase, they were not usually part of the development team. Answers from developers/hosts indicate that DSTs are not applied to their full potential. Deeper involvement of end users in the development phase could potentially increase the value and impact of DSTs. As a way forward, we propose streamlining the outputs of specific DSTs, so that they can be combined to a holistic insight of the consequences of management actions and serve the ecosystem approach in a better manner.

In the United States, there is a growing interest in the participatory development of agricultural and natural resource–focused decision support tools (DSTs). To provide greater insight for practitioners developing these DSTs, we conducted a review of manuscripts ( n = 23) that describe DSTs in US agricultural and forestry sectors, both those designed through participatory processes and otherwise. Our work operationalizes a novel conceptual framework developed to support participatory DST development, as recent scholarship suggests participatory processes lead to better adoption and use of DSTs. Our analysis suggests that tool developers should, in reporting on their efforts, more clearly articulate the ways decision makers are included in DST development, from problem identification through evaluation. Failure to do so limits our collective understanding of the utility of these tools. Following our review, we present recommendations for DST developers and other practitioners who want to support effective and transparent development of stakeholder-driven DSTs. We propose practitioners (1) implement complete assessments of relevant stakeholder network(s) that might use new DSTs; (2) engage stakeholders iteratively throughout the development process; (3) improve evaluation of DSTs, including an assessment of the usability, usefulness and usage of tools across their life cycle; and (4) and describe the process of stakeholder engagement process in published …