Current Operating Conditions Research Articles

Performance iterative learning control on nonlinear systems for tracking varying-scale and morphologically similar targets

This paper proposes a novel three-dimensional (3D) performance iterative learning control (PILC) scheme on nonlinear systems for tracking varying-scale and morphologically similar targets. The condition and performance weighted K-nearest neighbour (KNN) and the historical database update algorithms are developed to match the historical control inputs closest to the current operating conditions, and further realise the learning process along the performance domain. The control inputs of the PILC scheme can be updated by utilising the control information obtained from the time, iteration and performance dimensions. Also, the convergence property of the proposed framework is proved via the contraction mapping principle. Finally, compared with the existing ILC works, the illustrative simulation and experimental results are presented to illustrate that the PILC scheme has better tracking performance and robustness, smaller initial error, and faster convergence speed.

Analysis of the Electric Power Monitoring System in Wind Power Plants Based on Arduino Uno

The Wind Power Plant is a renewable energy solution that can reduce dependence on fossil fuels and reduce greenhouse gas emissions. However, to ensure operational efficiency and reliability, an effective monitoring system is needed to monitor and manage the electrical power produced. This resear chaims to develop and analyzean electrical power monitoring system at PLT Busing the Arduino Uno platform. The proposed monitoring system consist sofsophisti cated sensors integrated with Arduino Unotocollect real-time data regarding wind speed, output voltage, current and turbine operational conditions. The data collectedby these sensor sissentto the Arduino Uno, which then processes andstores thedata for further analysis. Additionally,this data canbe transmitted toot her devices for remote monitoring via a wire less communication module. Algorithms were developed to analyze and display critical information about turbine performance, including anomaly detection and failure prediction. System testing was carried out on a small-scale PLTB model, and the results showed that this system was able to monitor changes in electrical power with high accuracy and fast response time. Implementation of this Arduino Uno-based monitoring system also enable searly detection of potential problems, so that preventive action can be taken to reduce downtime and increase operational efficiency. The results of this research showthat using Arduino Uno as a platform for the electrical power monitoring system at PLTBis not only effective in collecting andanalyzing data,butis also economical andeasy to implement.This system canbeeasily adaptedand expanded to suit the specific needs of various types of PLTB. In conclusion, the Arduino Uno-based monitoring system offers an innovative and practical solution to improve the performance and reliability of PLTB.

A Data-Driven Online Prediction Model for Battery Charging Efficiency Accounting for Entropic Heat

This study proposes a charging efficiency calculation model based on an equivalent internal resistance framework. A data-driven neural network model is developed to predict the charging efficiency of lithium titanate (LTO) batteries for 5% state of charge (SOC) segments under various charging conditions. By considering the impact of entropy change on the open-circuit voltage (OCV) during the charging process, the accuracy of energy efficiency calculations is improved. Incorporating battery data under various charging conditions, and comparing the predictive accuracy and computational complexity of different hyperparameter configurations, we establish a backpropagation neural network model designed for implementation in embedded systems. The model predicts the energy efficiency of subsequent 5% SOC segments based on the current SOC and operating conditions. The results indicate that the model achieves a prediction error of only 0.29% under unknown charging conditions while also facilitating the deployment of the neural network model in embedded systems. In future applications, the relevant predictive data can be transmitted in real time to the cooling system for thermal generation forecasting and predictive control of battery systems, thereby enhancing temperature control precision and improving cooling system efficiency.

Evolution of Strategic Goal Setting in 2008–2023 and Requirements for Macroeconomic Policy in the Current Operating Conditions of the Russian Economy

Evolution of Strategic Goal Setting in 2008–2023 and Requirements for Macroeconomic Policy in the Current Operating Conditions of the Russian Economy

Impact of Future Low-Emissions Combustor Technology on Acoustic Scaling Laws

A first-of-its-kind examination of broadband noise associated with a far-term advanced low-emission aerocombustor concept is presented. Because of design trends and expected cycle changes for future aircraft propulsion systems, noise generated by sources in the combustor are expected to become of increasing significance for airport-community noise. The paper assesses the impact on legacy semi-empirical noise-prediction methods from the expected radical departures from current combustor operating conditions and designs, such as fuel/air distribution and flame anchoring techniques. Such methods are essential in system-level noise assessments at the preliminary design stage for advanced air transports to assure that overall environmental goals are met. Detailed unsteady pressure measurements, obtained in a fundamental combustion noise experiment using a combustor rig at relevant pressures and temperatures are analyzed. In addition to an advanced far-term low-emissions concept, a reference configuration with the test section arranged to model a modern combustor sector was also studied. For the test rig in the current-generation configuration, the measured broadband acoustic data are reasonably well described by the acoustic-power scaling laws used in legacy semi-empirical noise-prediction methods. For the future-advanced configuration, the legacy scaling laws, with some notable exceptions, provide correct trends, but with much less accuracy.

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

Simulation of the Performance of Li-Ion Batteries in DC/AC Operating Mode for Electric Vehicles

This work simulates the performance of lithium batteries for electric vehicles under different charge and discharge rates. The simulation is based on similarity factors for power, voltage, and current, reproducing the current operation conditions of an electric vehicle at the model scale. Most current driving modes are analyzed corresponding to discharge rates from 0.1C to 0.37C. The simulation also applies to determining the charging time using charge power in current conditions, from 6.1 kW to 18.3 kW (0.1C to 0.3C). Driving conditions derive from equations for vehicle motion, including all forces. Tests have been run under two configurations, continuous and alternate current circuits, to reproduce the two types of engines used by electric vehicles. The simulation shows good agreement in charge and discharge processes, with an average deviation of 3% related to current operating conditions and 1.6% between them, proving the validity of the simulation process.

Matching Characteristics of Refrigerant and Operating Parameters in Large Temperature Variation Heat Pump

Characterizing the optimal operating parameters for a heat pump with a specific refrigerant is paramount, as it provides valuable guidance for refrigerant selection. The temperature mismatch between cold and hot fluids in the evaporator and condenser can lead to degraded thermal performance in heat pumps with large temperature variations. To address these two key issues, we selected several pure refrigerants with varying critical temperature levels for use in a large temperature variation heat pump configuration. The corresponding thermal performance was then investigated using the Ebsilon code under fixed temperature lift conditions as the operating temperature varied. It indicates that the maximum coefficient of performance (COP) is typically achieved when the deviation factors of temperature and pressure from their critical parameters fall within the ranges of 0.62~0.71 and 0.36~0.5, respectively. Our research recommends the binary refrigerant mixture of R152a/R1336mzz(z) (COP = 3.54) for the current operating conditions, as it significantly improves thermal performance compared to pure R1336mzz (z) (COP = 2.87) and R152a (COP = 3.01). Through research on the impact of the compositional ratio of R152a/R1336mzz(z) on the thermal performance of the heat pump, we found that that the optimal ratio of R1336mzz(z) component to R152a component is 0.5/0.5. This study offers valuable guidance for selecting the most suitable refrigerants for heat pumps in practical engineering design scenarios.

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

This research aims to identify an effective feature-based pattern classification technique that uses vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This work employs two case studies utilizing eight datasets from Paderborn University to identify the bearing conditions of three and twelve classes. In this work, support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers with 10% holdout validation are applied to study 31 feature combinations. This study also examines the Henry gas solubility optimization technique for feature selection to identify the most discriminating features. The results indicate that four feature ensembles consisting of 2 to 5 features performed better with the support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with the highest accuracy of 99.8% and 99% using current signals for 3 and 12 classes respectively for combined current operating conditions.

Method for layout optimization of coastal cage aquaculture systems in Southeast Asia

This paper presents the development of a generally applicable method for the layout optimization of coastal cage aquaculture systems in Southeast Asia. The method combines data from the authors’ spatial-planning methodology with results of water quality modelling and an optimization technique based on genetic algorithms. This study successfully demonstrates the method's effectiveness for a target aquaculture site in northwest Bali, Indonesia. Optimum siting and sizes of fish farms to maximize fish production and promote fish growth while securing environmental sustainability are delivered for the site in Bali. The results of the application for several fish farming sizes show that by proper fish farming layout, environmentally sustainable fish production on the western channel of the site in Bali could rise tremendously with respect to the current operating conditions. Fish farm clusters with 256 cages are found to be best suited to the western channel. The method has broad applicability, can be upscaled to new sites, and can be used to assess, expand, and optimize currently operating sites.

Multi-parameter information detection of aircraft taxiing on an airport runway based on an ultra-weak FBG sensing array.

Tracking the moving state and analyzing the distribution feature of an aircraft taxiing on the pavement are crucial for runway safety management. One challenge is to provide a reliable methodology that would enable such needs to be addressed in long-distance range runway with high sensitivity and spatial resolution. This paper proposed a novel monitoring approach for acquiring multi-parameter information of aircraft taxiing on airport runway based on distributed vibration detected by ultra-weak fiber Bragg grating (FBG) sensing array. For an actual engineering equipped with the smart runway system, the ultra-weak FBG sensing technology was applied to collect the distributed vibration responses of taxiing aircraft in takeoff and landing stages. The monitoring data of the vibration sensing optical cables beneath the entire pavement were directly utilized to evaluate the identification feasibility and effectiveness of taxiing speed and position, landing distribution, and taxiing acceleration and distance based on samples of five representative aircraft types. The results indicated that the speed, position, aiming point offset, acceleration changes and probability distribution of the taxiing distance can be revealed based on actual measurements concerning the distribution of vibration intensity with respect to space and time. Moreover, the analysis demonstrated that the experimental results based on ultra-weak FBG sensing array was able to account for the characteristic of different aircraft types participating in the study and the current operating conditions of the airport.

Effect of using a ZnO-TiO2/water hybrid nanofluid on heat transfer performance and pressure drop in a flat tube with louvered finned heat exchanger

This study used an experimental setup consisting of a flat tube with a louvered finned crossflow configuration to examine the effects of utilizing a ZnO-TiO2-water hybrid nanofluid on heat transfer rate, heat transfer coefficient, Nusselt number, and pressure drop. The studies were carried out under laminar flow conditions (200 < Re < 800), at four different temperatures (50, 60, 70, 80 °C), four different volume concentrations of nanoparticles (0.025, 0.05, 0.1, 0.2%), and three different volume flow rates (4, 6, 8 LPM). The findings were compared with pure water (0%). The results indicate that using hybrid nanofluid improves the heat transfer performance and increases pressure loss in comparison with pure water. When comparing hybrid nanofluid to pure water, the largest increases in heat transfer rate, heat transfer coefficient, Nusselt number, and pressure drop were 87.8%, 21.7%, 26.4%, and 10%, respectively. In addition, it was found that, up to a specific value (0.05%), increasing the nanoparticle volume concentration enhanced the heat transfer rate, heat transfer coefficient and Nusselt number, but which began to decrease on increasing the concentration past this value. Therefore, it was concluded that nanoparticle volume concentrations greater than 0.05% negatively affect heat transfer under the current operating conditions. The maximum heat transfer rate, heat transfer coefficient, and Nusselt number were obtained under the conditions of an 8 LPM volume flow rate, 80 °C inlet temperature, and 0.05% volume concentration.

A CASE STUDY ON THE OPERATION AND MARKETING STRATEGIES OF GROUP HOME IN JAPAN

This study uses the Sky Group Home case study method to understand the current status of the industrial environment where the case is located, and explore the development trends and business strategies of Japanese companies in the group home market. It also analyses the corresponding marketing strategies of the industry based on regional expansion points, so as to improve the competitiveness of the industry's regional channels and open up new markets for international group home development. In recent years, Taiwan's group homes have been gradually privatized, and Japan's implementation experience has a high degree of reference value for my country's policy construction and group homes. This study focuses on operating groups in Japan Company A of Home House is taken as the research object, and the standard policies and operating models for the establishment of Japanese group homes are discussed through literature. Company A is used to understand its operating model based on its service content and current operating conditions. Environmental analysis and marketing mix analysis are conducted to explore its market, management and Marketing strategy; Finally, use SWOT analysis to summarize A The current situation of the company and suggestions for the future development of the industry. The legal entity creates a daily life environment similar to home, adhering to the core concept of "serving users", focusing on personal uniqueness, and providing users with social care in a small unit. In Japan, group homes gather two major groups: the elderly and the physically and mentally disabled, living in a house the size of an average family. Or apartments, with nursing staff working around the clock to provide living assistance, giving customers a convenient and secure living space and a comfortable and barrier-free daily life. After a preliminary understanding, I realized that compared to Taiwan, the development of group housing in Japan is more prosperous, and because the government adopts a "government subsidy, private operation" approach in the implementation of group housing welfare policies, it not only provides operating subsidies to private operators , and also provide consulting services and other assistance to the industry to make this social welfare industry more common in the country, so it was decided to further explore this policy.

Conceptual principles of the agri-food systems development in the conditions of Ukraine post-war recovery

The strategic post-war restoration conceptual principles of agri-food systems on the ensuring basis of the economic growth of the national econom and ensuring the country’s food security and the export potential formation of agro-food products with a high added value are developed in the article. The creation interdisciplinary analysis of added value and its orientation towards the achievement of the country’s Sustainable Development Goals was used as the basis for the development of measures system and practical actions for the strategic recovery of the agrifood sector. The current operating conditions of the agri-food sector is proved to require the strategic programs review and tools definition for its state support. It has been established that the agriculture share in many world countries is decreasing due to the industries development of deep processing of agricultural raw materials, population migration, however, in Ukraine, a rather high value of this indicator is observed with an insufficient amount of added value per worker. It was concluded about the comparative advantages trends restoration of Ukrainian agricultural products acquired in the past, the production growth dynamics, the exports expansion of deeply processed food products. In order to strengthen the studied sector position of the economy in world exports and overcome import dependence in technologies and material and technical resources, measures implementation for industries revival is foreseen on the basis of ensuring the agriculture orientation towards the «Agriculture 4.0» model implementation of scientific and technological development. The challenges of global climate changes, price volatility on the global agri-food market, martial law in the country, etc., which affect the development of the national agri-food system, are highlighted, and the main measures to minimize their negative impact are identified. To sum up, there is a need to calculate the resource potential amount of agriculture and develop a practical action system and measures to restore it to the pre-war period values, as well as the introduction of innovative technologies to increase crop capacity, productivity and reduce food losses. It has been proven that the development of the food industry should be oriented towards the technical and technological modernization of the existing capacities of the food industry and the creation of new factories for deep processing of agricultural raw materials. Measures set and tools for supporting food industry enterprises with the aim of diversifying the product range due to the production of healthy, functional food, deep processing products of agricultural raw materials has been substantiated. Key words: agri-food system, post-war recovery, agriculture, food industry, recovery strategy, agri-food.

Solar-Assisted Carbon Capture Process Integrated with a Natural Gas Combined Cycle (NGCC) Power Plant—A Simulation-Based Study

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC’s environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system’s technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region.

Effects of Ramp-Induced Incident Shock Impingement Under Various Slope Angle Configurations in a Strut-Based/Cavity-Assisted Supersonic Combustor

ABSTRACT The present study conducts a numerical investigation into the effects of ramp-induced shock impingement with different opening angles on the global flow-field and combustion behaviors within a strut-based/cavity-assisted scramjet combustor. To reproduce the intricate physical phenomena and all inflow properties of the primary channel, two-dimensional Reynolds-averaged Navier–Stokes (RANS) equations integrated with the shear stress transport (SST) k − ω turbulence model is implemented. The current approach considers several slope angle configurations of the ramp since they generate various shock systems that differ in magnitudes and coherent structures, substantially affecting the combustion efficiencies and chemical species distributions. Results reveal that an optimal shock angle of the ramp in correspondence with the current operating conditions can be achieved at 20°, which increases the combustion efficiency by 11% at the end of the cavity trailing edge without provoking excessive loss of total mass-flux-weighted pressure recovery. However, further expanding the slope angle above the optimum will trigger the flow blockage mechanism which enlarges the recirculation region on both sides of the ramp, simultaneously initiate their local thickening processes and create shock train areas owing to intensive interactions with the turbulent boundary layers. These phenomena are known for decreasing the total pressure ratio, adversely affecting all performance metrics of interest and destabilizing the main channel flow path.

Evaluación estadística del circuito de clasificación-molienda de la planta de carbón en pulpa de Antapite de la compañía Sierra Sun Group (Perú) empleando modeol s estadísticos de regresión y ANOVA

El presente trabajo pudo mostrar una evaluación estadística del circuito de molienda – clasificación de una planta aurífera de la planta Antapite de la Compañía Minera Sierra Sun Group (Perú). El circuito de molienda empleaba un molino de bolas, cuyo mineral descargado tenía una la ley de oro de 29.50 g/t, el cual alimentaba a un hidrociclón convencional Krebs (de 10 pulgadas de tamaño, 254 mm), cuyo rebose tenía una distribución de oro de 71.30 % (para tamaños de partículas de menos 74 μm). Sin embargo, el objetivo fue aumentar esta distribución a un valor equivalente a 77.39% (para partículas menores a 74 μm), es decir, al menos 5 puntos por encima de la actual. Este rebose alimentó al circuito metalúrgico formado por concentración gravimétrica y/o cianuración – adsorción dependiendo de la mineralogía con la que se encontraba asociada el oro. Se realizó un análisis estadístico de regresión múltiple para las variables de operación del hidrociclón Krebs de 10 pulgadas (254 mm) que permitió obtener la distribución de oro (objetivo mencionado anteriormente). De esta manera se plantearon tres escenarios diferentes, en los cuales se eliminaron las variables no significativas. El primer escenario evaluó la dependencia del tamaño de corte corregido d50C en función de la presión y caudal de la pulpa de alimentación al hidrociclón, el segundo escenario analizó la variable independiente: distribución de oro en el rebose del hidrociclón con respecto a las variables dependientes: caudal alimentado, presión, d50 y d50C para tamaños de partículas menores a 74 μm. Por último, el tercer escenario permitió evaluar la dependencia de la distribución de oro objetivo frente al d50C. Finalmente, se logró definir el parámetro que influye en la dependencia de la distribución de oro objetivo en el rebose, pudiendo encontrar así el valor de d50 (tamaño de corte sin corregir en micras) en función del caudal y presión de trabajo para llegar a la distribución de oro objetivo de 77.39% en el rebose (para partículas menores a 74 μm).

Modern sediment characteristics of Cua Dat hydroelectric reservoir based on high-resolution seismic data

The high-resolution seismic survey work was conducted in the reservoir area of the Cua Dat hydroelectric using the Sub-bottom Profiler EdgeTech 3400 equipment combination, covering a survey route of over 40 km. The collected data revealed the reservoir area's fundamental terrain and modern sediment characteristics. These findings provided insights into the dynamics of the sedimentary environment on the reservoir surface, indicating that the reservoir's current operational and exploitation conditions can ensure longevity and safety according to the design of the Cua Dat hydroelectric project.

Development of a Digital Twin Framework for a PV System to Resolve Partial Shading

Digital twin (DT) is a prolific buzzword in this era, where digitization plays a significant role. The perception of solar energy harvesting has been gaining popularity with the advent of solar panels. Solar asset maintenance is a need of the hour for investors because of the smart city scheme and green building certificate evaluation for all industries, educational institutions, etc. Among the list of factors that reduce PV system efficiency, the issue of partial shading is a vital distress that must be resolved. This paper focuses on the development of a digital twin framework that is proactively driven by shading patterns and a proposed optimization-based reconfiguration embedded controller that electronically relocates the panel in the physical world. The real-time system has been created for a three-by-three series parallel panel arrangement. The proposed switching matrix controller achieves the maximum power, i.e., 40% of increased power output, row current difference is made almost zero from 3 to 4 A, and fill factor increases by 20% by reconfiguring the solar array. It is done based on a decision taken by a nature-inspired (equal irradiation distribution algorithm) or puzzle-based (skyscraper) optimization algorithm. Switching matrix controllers overcome the disadvantages of physical relocation. The users can query the digital twin build to know the historical performance and current operating conditions of the system. It can trigger alarms as early warnings and make predictions about possible system anomalies, if and when they occur using a digital twin.

Planning Purposeful Activities Autonomous Intelligent Robot with Knowledge Update in Short-Term Memory

The main problems associated with the creation of autonomous intelligent robots capable of performing various complex tasks in a priori undescribed unstable problematic environments, based on the processing of knowledge presented in an abstract way, are outlined. To store typical elements of an abstract knowledge representation model, the article recommends using long-term and short-term memory. Long-term memory with associative search and data retrieval is designed to permanently store information necessary for planning a variety of purposeful activities that provide the robot with the ability to solve various complex behavioral tasks. In short-term memory, submodels of knowledge representation are entered from long-term memory, which are necessary for solving the current task of a certain type in the short term, related to the fulfillment of the task formulated for the autonomous intelligent robot. At the same time, with each change in the type of the current task of behavior being solved by an autonomous intelligent robot, a corresponding update of knowledge stored in short-term memory is simultaneously carried out. Original constructions of typical elements of the model for representing abstract knowledge in the form of various behavioral skills, set regardless of a particular subject area, have been developed. This approach to building a knowledge representation model allows autonomous intelligent robots to adapt to the current operating conditions and, on this basis, organize purposeful activities in complex unstable problematic environments. Various tools and rules for processing abstract knowledge are proposed, which endow autonomous intelligent robots with the ability to eliminate the differences between the current and target situation of the problem environment both in terms of the values of structurally equivalent relations of the same name in them, and in the current states of objects in the environment. This, in turn, makes it possible to create intelligent problem solvers for autonomous intelligent robots for various purposes, capable of performing complex tasks in unstable a priori uncertain conditions of a problematic environment.