Decision Support System For Operations Research Articles

Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.

Vision-based normalized canopy area estimation for variable nitrogen application in apple orchards

Normalized canopy area is a measure of spatial canopy fill or canopy coverage in a tree. It is an important factor for growers to consider in making various farming decisions, such as timing and amount of fertilization, pesticide application, and irrigation, as it gives a sense of the overall canopy growth. This study focused on estimating the normalized canopy area of apple trees using a ground-based stereo-vision system in a commercial orchard under natural lighting conditions. A color, depth, and height threshold, followed by a point density-based outlier removal was applied to segment the target tree canopies. The segmented canopy area was compared with the area estimated with manual segmentation, which showed an F1 score of 0.78. The overall growth of the tree canopies was then represented using normalized canopy area, which was calculated as a fraction of pixels occupied by the tree foliage over the total possible area a tree could occupy for a given tree spacing. It was found that the normalized canopy area of individual trees was highly correlated with the surface volume of the canopy (r = 0.7), which is a traditionally used parameter to assess or represent canopy growth. The estimated normalized canopy area was then compared against experts' nitrogen recommendation for the sample trees, which was based on their visual assessment of the vigor/growth of the trees. Four individual experts (one apple farmer, one orchard manager, and two horticulturists with research and outreach experience) were asked to provide their independent nitrogen recommendation and an average recommendation level was calculated. The comparison showed an overall correlation coefficient of -0.5, indicating an opposite relationship (higher the canopy density, lower the need for nitrogen and vice versa) between the normalized canopy area and the average of the experts' nitrogen recommendation. When the relationship between the normalized area and nitrogen recommendation level was assessed for individual experts, the correlation coefficient was estimated to be -0.86, -0.84, -0.96, and -0.78, respectively, for Expert1, Expert2, Expert3, and Expert4 respectively. The results from this study identified a critical measure for assessing nitrogen needs at individual tree levels in apple orchards and provides an automated tool to estimate the measure. The outcomes of this study could be used as a part of an integrated decision support system for orchard operations (e.g., fertilization or irrigation) that may include additional canopy characteristics such as spectral signature, trunk diameter, and rate of change of leaf color in the fall.

A decade of urban fires: Portuguese events between 2013 and 2022

This study describes a dataset containing urban fire events that took place in mainland Portugal between 2013 and 2022. The Regulation n.º3317-A/2018, established by the Portuguese National Emergency and Civil Protection Authority (Autoridade Nacional de Emergência e Proteção Civil, ANEPC), defines the Operations Management System (Sistema de Gestão de Operações, SGO). Among other attributions, this system allows to manage the lyfe-cycle of the urban fire events, from ignition to extinction, through the Operations Decision Support System (Sistema de Apoio à Decisão Operacional, SADO). This system supports the systematic collection of a minimum set of data on each event. All instances included in the dataset were retrieved from SADO. To make the data suitable for analytic purposes, several pre-processing actions were taken, including the steps of data transformation and cleaning. The dataset was further validated by a set of technical procedures aiming to verify both data correctness and utility. The final dataset provides the most recent multi-year record of Portuguese urban fires including 27 variables on 72641 events.

Decision Support Framework for Optimal Reservoir Operation to Mitigate Cyanobacterial Blooms in Rivers

Flow control flushing water from reservoirs has been imposed in South Korea for mitigating harmful cyanobacterial blooms (CyanoHABs) in rivers. This measure, however, can cause water shortage in reservoirs, as the measure adopting this flow control may require an additional amount of water which exceeds the water demand allocated to the reservoirs. In terms of sustainability, a trade-off between improving water quality and alleviating water shortage needs to be considered. This study aimed at establishing a practical framework for a decision support system for optimal joint operation of the upstream reservoirs (Andong and Imha) to reduce the frequency of CyanoHABs in the Nakdong River, South Korea. Methodologically, three models were introduced: (1) a machine learning model (accuracy 88%) based on the k-NN (k-Nearest Neighbor) algorithm to predict the occurrence of CyanoHABs at a selected downstream location (the Chilgok Weir located approximately 140 km downstream from the Andong Dam), (2) a multiobjective optimization model employing NSGA-II (Nondominated Sorting Genetic Algorithm II) to determine both the quantity and quality of water released from the reservoirs, and (3) a river water quality model (R2 0.79) using HEC-RAS to simulate the water quality parameter at Chilgok Weir according to given upstream boundary conditions. The applicability of the framework was demonstrated by simulation results using observational data from 2015 to 2019. The simulation results based on the framework confirmed that the frequency of CyanoHABs would be decreased compared with the number of days when CyanoHABs were observed at Chilgok Weir. This framework, with a combination of several models, is a novelty in terms of efficiency, and it can be a part of a solution to the problem of CyanoHABs without using an additional amount of water from a reservoir.

An algorithmic application with flexible airspace approach

PurposeThe purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm.Design/methodology/approachIn this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode.FindingsIt has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO2 emission savings.Originality/valueAs far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.

Real-time operating decision support system for reservoir operation using weather forecast and a hydrological model

This paper presents the application of a real-time decision support system (DSS) for power generation and flood control at the Thac Xang hydropower plant in Lang Son province, Vietnam. The DSS consists of three key components: real-time operation and visualization, forecast, and optimized decision modules. The real-time operation and visualization module provides operators with a graphical interface that displays real-time data on the plant's status, such as water levels, reservoir levels, and power output. The weather forecasting module provides information about future precipitation. This information is used as input data for the inflow forecasting model. The real-time computing module takes into account current conditions, such as the water level in the reservoir to compute relevant variables such as current inflow rate or outflow rates from the reservoir. The simulation module is used to test different operating scenarios taking current and future inflow rates into account. This helps the plant operators to understand the impact of their decisions on the plant's performance. The simulation module can also be used to identify potential problems and develop contingency plans. Therefore, a mathematical model is developed. Particularly, this model uses real-time information of dams. The main objective is to maximize economic value over the time horizon by producing electricity when it is most valuable. An approach of a simulated annealing algorithm is used to solve this model. We also present a decision support software tool for small hydropower systems that provide hydropower equipment operators with the information required to optimize the dam's performance in terms of power efficiency and effectiveness.

Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry

This work proposes a new sustainable operational performance optimization-based decision support system for assessing the failure behaviour of the Boiler Unit (BU) in the milk process industry. A novel fuzzy Jaya Lambda–Tau Optimization (JLTO) approach-based modelling was established for tabulating various reliability indices of BU. Fuzzy Set (FS) theory-based concepts were applied to consider the vagueness/uncertainty involved in the collected data. JLTO approach-based results show decrease in the boiler unit’s availability with the increase in uncertainty level. Further, to improve the boiler unit’s availability using qualitative data obtained from expert’s opinion, Failure Mode and Effect Analysis (FMEA) was performed. To address the shortcomings of conventional FMEA approach-based results and to pinpoint the riskier failure causes contributing to decrease in system’s availability, Fuzzy-Complex Proportional Assessment (FCOPRAS) decision-making approach was integrated with FMEA approach. Fuzzy Combinative Distance-based Assessment (FCODAS) approach was also applied to compare the ranking results for accurate decision-making. The proposed integrated framework predicts the failure behaviour with high accuracy by considering the uncertainty and finding the riskier failure causes of the considered system which is one of the main drawbacks of existing FS theory-based integrated models. The proposed model-based results will help in developing an optimal digitalized maintenance schedule, which will be beneficial for improving the overall performance of the considered unit. The digitalized maintenance schedule will also be useful for BU of various other industries across the globe. Sensitivity analysis was also done for assessing the robustness of the proposed sustainable operational performance optimization-based decision support system.

Study of determining methods and preventing emergency conditions for the flight of an pilotless vehicle in case of failure of onboard equipment

The analysis of the influence of internal and external factors affecting the flight safety of a pilotless vehicle is carried out. The assessment modeling of the aviation accident treat in failures case of the pilotless vehicle onboard equipment and the deterioration of the flight weather conditions is carried out. A method to prevent the threat of the emergency situation under consideration when controlling a pilotless vehicle, taking into account the peculiarities of its operation, is proposed. The obtained results make it possible to evaluate the possibility of applying methods for assessing and preventing the threat of an aviation accident during the flight of a pilotless vehicle. The proposed methods and simulation results are used in the design of pilotless vehicles, as well as onboard devices for ensuring aircraft flight safety. Keywords pilotless vehicle, onboard flight safety management system, assessment of flight conditions, counteracting the threat of an aviation accident, operator decision support system

JETSON NANO DEVKIT BASED DECISION SUPPORT SYSTEM DESIGN FOR SPRAYING STRATEGY AND SPRAYING SYSTEM OF AGRICULTURAL UNMANNED AERIAL VEHICLES

Aim: The aim of this paper is to create an intelligent decision support system for spraying and fertilization operations in agricultural lands with Agricultural Unmanned Aerial Vehicles. In this system, different pesticides will be sprayed in sparse or undeveloped areas, different pesticides will be sprayed in areas with normal development and different pesticides will be sprayed in areas with intensive development and plant health will be protected. Thus, inadequate spraying or burning of the plant due to over spraying will be prevented. Method: In this paper, a decision support system embedded in the Jetson Nano Devkit Artificial Intelligence Computer is developed for spraying and fertilization operations performed by Agricultural Unmanned Aerial Vehicles (UAVs). Three different image processing algorithms ("RGB Filter Method", " Pixelwise Classification Method" and "HSV Range Filter Method") were tested on 4000 agricultural field images in order to accurately analyze the snapshots obtained from UAVs. Solution time and solution reliability (green capture rate) were considered as two different objectives in this system where the images should be evaluated instantaneously and the amount of pesticide spraying should be changed. According to the image processing results, the working principle of the spraying system, which was created according to 3 different levels (low-medium-high level) and "Pulse Width Modulation" and "Pulse Width Modulation" techniques, was simulated through the "Proteus" simulation programme and transferred to the real system. Results: As a result of the analysis, it was determined that the "RGB Filter Method" is the ideal algorithm that simultaneously meets the objectives of solution time and solution reliability. In the spraying system, the amount of pesticide to be sprayed per second was determined for 3 stages. Finally, it was decided to take an image every 6 seconds from the camera on the APC and to spray accordingly. Conclusion: Thanks to the proposed decision support system, agricultural areas can be sprayed at dynamic levels according to the level of plant needs. In today's world of global warming, soil erosion and water scarcity, the study is expected to make a positive contribution to smart agricultural practices.

Internet of Things Impact on Supply Chain Management

Traditional supply chains deal with uncertainty, cost, complexity, and vulnerability problems. In order to overcome these problems, supply chain operations need to be innovative in business process management. Establishing a large-scale smart infrastructure to merge data, information, products, physical objects, and all supply chain processes needs to automate intelligently in supply chain management (SCM) through building secure operational decision support systems. Information technology (IT) and information and communication technology (ICT) are essential in business process automation. In recent decades, Internet of things (IoT) applications have played an essential role in automating supply chain management's regular business processes and their operations-related decisions. The vast adoption of wireless sensor networks (WANs) with IoT technology provides access to sensor nodes and sensing ability in Internet-connected devices. This way, the IoT provides access to a vast amount of data gathering by the WSNs, over the wide-area data communication networks. For example, sensing enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the IoT-based infrastructure. This paper reviews some of the essential characteristics of IoT technology for designing SEs in supply chain management.

Improvement of Train Traffic Control Technology Based on Abstract Modelling of Operational Processes   

 Purpose. The main goal of the work is to improve the process of controlling transport units (trains) on the basis of abstract modelling of operational processes, which will allow the operational dispatching apparatus to respond in a timely manner to constantly changing train conditions. Methodology. In order to ensure the rhythmic and uniform movement of trains of all categories along railway lines, train dispatchers, based on their experience, together with locomotive dispatchers and train locomotive drivers, constantly monitor the operational train condition and develop a strategy for the movement of trains along railway sections. This approach is quite energy-consuming in terms of human resources due to excessive nervous tension. As a result of this shortcoming and mistakes made, there are unproductive downtime at railway stations and, in some cases, a significant reduction in sectional speed, which in turn directly affects the industry's profitability and the quality of passenger service, including high-speed traffic. The basis for making management decisions on operational train conditions is automated systems and personal experience of professionals. In this regard, it is advisable to form a model that will reproduce the optimal train operation plan by predicting the main indicators. Findings. In the course of the scientific and applied research, the predictor of collision of trains of different categories with station and inter-train intervals was determined, which can later become the basis of a powerful module of the operational decision support system. Originality. The paper proposes an approach to improving the process of managing transport units based on abstract modelling of operational processes, which, unlike existing approaches, allows the implementation of a high-speed intelligent decision support system for railway dispatching with the possibility of self-adaptation. Practical value. The implementation of the proposed approach in the form of an automated software system will further increase the profitability of the railway industry in the freight and passenger traffic sector.

Ant colony optimization for path planning in search and rescue operations

In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper, we address the problem of efficiently optimizing search paths in the context of the NP-hard optimal search path problem with visibility, based on search theory. To that end, we evaluate and develop ant colony optimization algorithm variants where the goal is to maximize the probability of finding a moving search object with Markovian motion, given a finite time horizon and finite resources (scans) to allocate to visible regions. Our empirical results, based on evaluating 96 variants of the metaheuristic with standard components tailored to the problem and using realistic size search environments, provide valuable insights regarding the best algorithm configurations. Furthermore, our best variants compare favorably, especially on the larger and more realistic instances, with a standard greedy heuristic and a state-of-the-art mixed-integer linear program solver. With this research, we add to the empirical body of evidence on an ant colony optimization algorithms configuration and applications, and pave the way to the implementation of search path optimization in operational decision support systems for search and rescue.

Artificial Neural Network (ANN) Modelling for Biogas Production in Pre-Commercialized Integrated Anaerobic-Aerobic Bioreactors (IAAB)

The use of integrated anaerobic-aerobic bioreactor (IAAB) to treat the Palm Oil Mill Effluent (POME) showed promising results, which successfully overcome the limitation of a large space that is needed in the conventional method. The understanding of synergism between anaerobic digestion and aerobic process is required to achieve maximum biogas production and COD removal. Hence, this work presents the use of artificial neural network (ANN) to predict the COD removal (%), purity of methane (%), and methane yield (LCH4/gCODremoved) of anaerobic digestion and COD removal (%), biochemical oxygen demand (BOD) removal (%), and total suspended solid (TSS) removal (%) of aerobic process in a pre-commercialized IAAB located at Negeri Sembilan, Malaysia. MATLAB R2019b was used to develop the two ANN models. Bayesian regularization backpropagation (BR) showed the best performance among the 12 training algorithms. The trained ANN models showed high accuracy (R2 > 0.997) and demonstrated good alignment with the industrial data obtained from the pre-commercialized IAAB over a 6-month period. The developed ANN model is subsequently used to create the optimal operating conditions which maximize the output parameters. The COD removal (%) was improved by 33.9% (from 68.7% to 92%), while the methane yield was improved by 13.4% (from 0.23 LCH4/gCODremoved to 0.26 LCH4/gCODremoved). Sensitivity analysis shows that COD inlet is the most influential input parameters that affect the methane yield, anaerobic COD, BOD and TSS removals, while for aerobic process, COD removal is most affected by mixed liquor suspended solids (MLSS). The trained ANN model can be utilized as a decision support system (DSS) for operators to predict the behavior of the IAAB system and solve the problems of instability and inconsistent biogas production in the anaerobic digestion process. This is of utmost importance for the successful commercialization of this IAAB technology. Additional input parameters such as the mixing time, reaction time, nutrients (ammonium nitrogen and total phosphorus) and concentration of microorganisms could be considered for the improvement of the ANN model.

A decision support system for safer airplane landings: Predicting runway conditions using XGBoost and explainable AI

The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classification model to identify slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. The XGBoost models are combined with SHAP approximations to provide a reliable decision support system for airport operators, which can contribute to safer and more economic operations of airport runways. To evaluate the performance of the prediction models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy.

Optimizing Operational-Level Forest Biomass Logistic Costs for Storage, Chipping and Transportation through Roadside Drying

Forest biomass (FB) could supply more of Australia’s energy needs, but delivered costs must be reduced for it to be a viable energy source. Operational planning is critical to reducing delivered costs as it determines actual activities, though few operational FB supply chain (FBSC) planning tools have been published. This paper presents a “proof-of-concept” operational FBSC decision support system (DSS) to schedule FB deliveries for eight weeks from roadside storage for the least cost, taking in account moisture content changes. Four mathematical models are compared, solving a linear formulation of the FB delivery problem in terms of solution speed and delivered cost, and the practicality of implementing the solutions. The best performing model was a Greedy algorithm as it produced solutions not significantly different from those of the tested linear programming solver and was readily modified to significantly improve solution implementation through the addition of a non-linear element. FBSC planning tools typically assume accurate knowledge of stored FB quantities and that little or no rainfall occurs during storage. In practice, stored FB quantity estimates can be inaccurate due to variation in the bulk density of the piles. Improving these estimates is a critical area for future research. This study found that simulated rainfall with <20 mm during the first week of the scheduled period did not significantly effect delivered costs.

Research on the decision support system for optimal operation of large reservoir power generation

In order to give full play to the efficiency of power generation and improve the utilization rate of water resources, the optimal operation of reservoir power generation has become one of the hot researches by the experts and scholars. Luntan Key Water-Control Project is taken as an example, which gives consideration to the comprehensive benefits of flood control and power generation. Based on building both the mathematical models of flood pre-release control and the medium-long term power generation, a decision support system with fast online optimization is developed by using SOA architecture and J2EE technology. The results show the system can improve the operation level of the reservoir power generation, reduce effectively the water abandoning rate, realize the scientific optimal operation, provide scientific basis for the management decision, and create more comprehensive benefits.

Operation and Maintenance Decision Support System for Photovoltaic Systems

Operation and maintenance (O&M) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An O&M decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 €/kW/year when performing O&M actions. For an electricity price of 59.98 €/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized O&M cost value of 7.45 €/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV O&M Cost Model.

Advanced Technologies for Supporting Operational Decision-Making in Civil Aviation

In civil aviation, many tasks require the use of modern technologies, in particular artificial intelligence technologies. The features of the crew and dispatcher decision-support tasks that can be solved based on deep neural networks are considered. The features of the intelligent crew interface for improving interaction with the operational decision support system are considered. Optimization of air traffic management by using genetic algorithms is illustrated by the example of two tasks. A special feature of the genetic algorithm for solving the problem of optimizing the sequence and landing times of arriving aircraft is a non-standard crossing operator. A special feature of the genetic algorithm for solving the problem of constructing conflict-free trajectories of a given length at low altitude in conditions of complex terrain is the method of forming the objective function. In conclusion, there is a list of problems that need to be solved for the wider use of artificial intelligence in civil aviation.

TOQO: A new Tillage Operations Quality Optimization model based on parallel and dynamic Decision Support System

Mechanization in agriculture involves all stages of cultivating and preparing innovations, from basic and essential hand devices and tools to more complex and mechanized implements. Fundamentally, such devices and tools enable agricultural activities to initiate different crop yields in many different neighborhoods in the world's ecological system. Due to the unstable nature of fuel prices, market demands, and damaging effects of tillage machinery vibrations on the operator, exploring efficient methods of preventing these situations has become indispensable. There are many existing methods related to tillage optimization, including fast, high quality, and cost-effective tilling. However, none of the existing methods perform real-time and dynamic recommendations due to the inability to provide real-time tillage data. Another issue is the unavailability of a tillage recommendation model that acts based on the changes of a fixed set of tillage parameters and provides the necessary guidance to the tractor driver during the tillage operation. This paper proposes a Tillage Operations Quality Optimization (TOQO) model that aims to improve the efficiency of tillage operations. The model provides real-time tillage data through integrating the Internet of Things (IoT) technology and a dynamic Decision Support System (DSS) for tillage machinery operation and process optimization. The proposed model has been successfully implemented, tested, and evaluated in real-world tillage operations. The results of this TOQO study clearly show improvements in the tillage operations. This is because the TOQO gives online recommendations in real-time based on dynamic measurements of six tillage performance evaluation parameters. The parameters are vibration, bulk density, slippage ratio, fuel consumption, real tillage depth, and field efficiency. The TOQO model successfully handles more than one tillage machinery tractor by using cloud computing services. Vibration increases when using the system on the X-axis at 46.63843 rms, Y-axis at 23.23612 rms, and the Z-axis at 51.47240 rms. Vibration fractures the soil, reducing soil cohesiveness when using an oscillating tillage tool and producing smaller soil aggregates than a non-oscillating one. When using the system, the value of the virtual density increases by 0.23821 g/cm3. Most of the bulk density values for the optimized phase are within the recommended or required growth range. There is a slight increase in depth by 0.4279 cm and a decrease in slippage ratio by 0.64364%. Tractor fuel consumption decreases by 1.2169 L per hour, indicating a reduction in tractor wheel slippage. Reduced fuel consumption and wheel slippage of tillage operation are essential parameters to increase field efficiency and reduce tillage operation costs.

Next-Generation Smart Response Web (NG-SRW): An Operational Spatial Decision Support System for Maritime Oil Spill Emergency Response in the Gulf of Finland (Baltic Sea)

The Baltic Sea is a unique and sensitive brackish-water ecosystem vulnerable to damage from shipping activities. Despite high levels of maritime safety in the area, there is a continued risk of oil spills and associated harmful environmental impacts. Achieving common situational awareness between oil spill response decision makers and other actors, such as merchant vessel and Vessel Traffic Service center operators, is an important step to minimizing detrimental effects. This paper presents the Next-Generation Smart Response Web (NG-SRW), a web-based application to aid decision making concerning oil spill response. This tool aims to provide, dynamically and interactively, relevant information on oil spills. By integrating the analysis and visualization of dynamic spill features with the sensitivity of environmental elements and value of human uses, the benefits of potential response actions can be compared, helping to develop an appropriate response strategy. The oil spill process simulation enables the response authorities to judge better the complexity and dynamic behavior of the systems and processes behind the potential environmental impact assessment and thereby better control the oil combat action.