Uncontrolled Inputs Research Articles

Inference of Robust Reachability Constraints

Characterization of bugs and attack vectors is in many practical scenarios as important as their finding. Recently, Girol et. al. have introduced the concept of robust reachability, which ensures a perfect reproducibility of the reported violations by distinguishing inputs that are under the control of the attacker (controlled inputs) from those that are not (uncontrolled inputs), and proposed first automated analysis for it. While it is a step toward distinguishing severe bugs from benign ones, it fails for example to describe violations that are mostly reproducible, i.e., when triggering conditions are likely to happen, meaning that they happen for all uncontrolled inputs but a few corner cases. To address this issue, we propose to leverage theory-agnostic abduction techniques to generate constraints on the uncontrolled program inputs that ensure that a target property is robustly satisfied. Our proposal comes with an extension of robust reachability that is generic on the type of trace property and on the technology used to verify the properties. We show that our approach is complete w.r.t its inference language, and we additionally discuss strategies for the efficient exploration of the inference space. We demonstrate the feasibility of the method and its practical ability to refine the notion of robust reachability with an implementation that uses robust reachability oracles to generate constraints on standard benchmarks from software verification and security analysis. We illustrate the use of our implementation to a vulnerability characterization problem in the context of fault injection attacks. Our method overcomes a major limitation of the initial proposal of robust reachability, without complicating its definition. From a practical view, this is a step toward new verification tools that are able to characterize program violations through high-level feedback.

Archetype Building Energy Models for Residential Sector: Importance of Uncontrolled Inputs in Predictive Control and Energy Flexibility

A 1st-order gray-box archetype is developed using high-quality measurements from an unoccupied experimental house to investigate the sensitivity of economic model-based predictive control performance under Québec’s dynamic electricity tariff for the residential sector, rate Flex-D, to uncertainty in the weather forecast. The archetype incorporates the outside air temperature and the solar gain as uncontrolled heat sources and has three parameters: the overall thermal capacitance, the thermal resistance to the outside air temperature, and the solar aperture. The performance of economic model-based predictive control is compared under the uncertain day-ahead forecast and the historical measurements of the same day. The results show that the most influential uncontrolled input directing the performance of economic model-based predictive control under rate Flex-D is the price signal, which makes the impact of uncertainty in the weather forecast almost negligible.

ОПТИМАЛЬНІ ОЦІНКИ ВЕКТОРА СТАНУ ДЛЯ ДИСКРЕТНИХ СТОХАСТИЧНИХ СИСТЕМ З НЕВИЗНАЧЕНИМИ ЗБУРЕННЯМИ ТА ШУМОМ

Assessment of the dynamic systems state is widely used in various areas of technical activity. In practice, the most well-known and common methods of estimation are the methods of the Kalman filter and Luenberger observers. Most of the results known in the scientific literature for constructing estimates of the dynamic systems state in the presence of acting uncontrolled disturbances and noise are associated with stationary systems. The insensitivity of such estimates to the influence of accompanying uncontrolled perturbations is ensured by the introduction of certain restrictions that are imposed on a number of system matrices, and their optimality is achieved by minimizing the estimation errors covariance matrix trace due to those degrees of freedom that remained after the separation procedure was performed. The aim of the work is to develop a filter capable of generating state vector optimal estimates of a stochastic linear system with changeable parameters that are insensitive to the influence of uncontrolled inputs. In this case, the conditions that guarantee the convergence of the estimates obtained must be easily verifiable. The goal is achieved by using a one-to-one transformation of the equations of systems, followed by the application of the Kalman filter. The O'Reilly functional observer is used as the specified transformation. An example is given that illustrates the effectiveness of the proposed filter.

The Biological, Histopathological, and Parasitic Abundance of Oreochromis niloticus Inhabiting Two Different Freshwater Canals

The current study aimed to assess the impact of metal pollution in water on parasitic abundance, biology, and histopathological characteristics of Oreochromis niloticus in two different freshwater habitats in the Al Sharkia government. The fish were collected from the Mueweis canal area (A) which received industrial wastewater from factories and the San El-Hagar area (B) which received agricultural and domestic wastewater. Parasitic abundance and histopathological changes in the liver and gonads were inspected. The total prevalence of parasitic infection was at the highest percentage in area B correlated with metals present in the water, in addition to severe histopathological damage to the liver and the gonads. The prevalence of parasites for different examined fish ranges from 50% for parasites in the San El-Hagar canal and 4.17% for parasites in the Muwies canal, seasonally. There were positive relationships between Fe or Zn or Mn concentrations with parasite abundance in tilapia fish collected from the San Hagar canal. Several histopathological alterations were detected in the liver and gonads of O. niloticus collected from the two canals located in the Al Sharkia province. It was concluded that the uncontrolled inputs of agricultural and domestic wastes highly altered the O. niloticus health status and the prevalence of the parasites in the investigated two areas.

PENGUKURAN DAN BENCHMARKING EFISIENSI PARIWISATA DI PULAU JAWA-BALI-NUSA TENGGARA MENGGUNAKAN DATA ENVELOPMENT ANALYSIS TAHUN 2013-2017

Tourism resources owned by the government and the private sector often ignore the proper aspects.The use of uncontrolled inputs is the beginning of the inefficiency of tourism activities, which impact the non-optimal output produced. This research aims to identify the efficiency level of government financial management in the tourism sector supported by the provision of tourism facilities to tourist visits and regional revenues from the tourism sector in Java, Bali, and the Nusa Tenggara Islands. The research method used is Data Envelopment Analysis Input-Oriented Constant Return to Scale Model with a quantitative descriptive approach. The results showed that the provinces of DKI Jakarta, West Java, Central Java, and Banten were relatively efficient in optimizing tourism financing. DKI Jakarta and Banten tended to have stable efficiency, and they are good to be referenced.Managerially, this research offers several suggested recommendations. The local government and private sectors can benchmark the efficiency of the tourism sector based on the pattern of financial management for tourism and the use of other regional inputs identified as efficient. Improving efficiency can be done by minimizing the use of inputs at a fixed level of output. Minimum input is expected to reduce the total cost. Theoretically, the result of this study is very helpful in efforts to develop methods of measuring efficiency in the tourism sector that has not been widely implemented in Indonesia.

ISS-like properties in Lie-bracket approximations and application to extremum seeking

In this paper, we consider a class of nonlinear time-varying systems that are subject to uncontrolled inputs. Those inputs may represent, for instance, disturbances, time-varying parameters, or model uncertainties. Exploiting the stability properties of an extended system, a so-called Lie-bracket system, we derive stability properties of the original system. Those stability results, in the spirit of input-to-state stability, are then applied to the case where the dynamical system is an extremum seeking system. Two scenarios are analyzed. We first consider that the uncontrolled input is a deterministic noise corrupting the cost measurement. We then examine the case where the uncontrolled input induces time-variations of the cost optimizer.

The state of persistent organic pollutants in South African estuaries: A review of environmental exposure and sources

The long-term health of many South African estuaries is impacted by pollutants entering these systems through industrial and agricultural runoff, sewage outfalls, contaminated storm water drainage, flows from informal settlements, and plastic materials in marine debris. Uncontrolled inputs combined with poor environmental management often result in elevated levels of persistent organic pollutants (POPs) in affected estuaries. Data on POPs research from 1960 to 2020 were analysed in terms of their sources, environmental investigations, and health implications. The outcome showed polychlorinated biphenyls (PCBs) and per- and poly-fluoroalkyl sulphonates (PFASs) to exceed the US EPA health advisory levels for drinking water. Concentration of organochlorine pesticides (OCPs) in water were below the WHO limits, while those in fish tissues from most estuaries were found to be below the US FDA limits. Although environmental compartments in some estuaries (e.g. Rooiels and uMngeni estuaries) seem to be less contaminated relative to other marine systems around the world, many others were polluted and critically modified (e.g. Durban Bay, Swartkops, Sundays, and Buffalo systems). Due to inconsistent monitoring methods coupled with limited data availability, temporal trends were unclear. Of the 290 estuaries in South Africa, 65 were prioritised and recommended for POPs evaluation based on their pollution sources, and a monitoring strategy was defined in terms of sampling. Government policies to curb marine pollution need to be enforced to prevent chronic contamination that leads to water quality deterioration and loss of ecosystem services.

Context-aware Model Predictive Control framework for multi-zone buildings

Today, Model predictive control (MPC) has largely been used to optimize energy consumption and maintain thermal comfort in buildings. However, to build an online MPC model in building, the dynamics of the physical system must be accurately modeled, which is a time-consuming and costly task. Neural network models help to overcome the modeling problems especially with the availability of historical data. This research presents a novel online data-driven control framework named Model Predictive Control via Genetic algorithm (MPC-GA) allowing the optimal operation of the heating, ventilation, and air conditioning system and has been experimentally validated in a multi-zone retail building. The MPC-GA combines an attention-based neural network time series multivariate prediction model with a MPC framework. The prediction model used a dual-stream neural networks based on multivariate time series of controlled and uncontrolled inputs. The attention mechanism is applied on controlled parameters to give them more weight to better predict the zone temperature. The prediction model is used as input for the optimization framework which minimizes: energy consumption, peak demand and discomfort during occupied hours under self-tuned setpoint, temperature ramp and equipment cycling constraints. A heuristic search algorithm using a genetic algorithm is used to solve the online data-driven MPC-GA models and obtain the future optimal combination settings of all controls for all the zones over a prediction horizon. The benchmark results showed that the MPC-GA outperforms RBC control systems with more than 50% and 80% reduction in energy consumption and discomfort respectively.

Tuning of Multivariable Model Predictive Control for Industrial Tasks

This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant.

Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms

A modifier adaptation scheme based on Gaussian processes is presented to optimize the control inputs of a wind farm. Often an approximate model of the wind farm is available, however due to the high complexity of the process plant-model mismatch is prevalent. For example the mechanics of wakes is not well-understood, which may have a profound impact on the power production of wind farms. Therefore, Gaussian process (GP) regression is exploited to account for this deviation. A distributed learning approach is used to learn the plant-model mismatch of each individual turbine considering explicitly the uncertainty of the uncontrolled inputs, like the wind direction. Afterwards, a distributed optimization scheme using alternating direction method of multipliers is applied to iteratively attain the wind farm optimum despite the presence of plant-model mismatch.

Histopathological alterations and parasitic infection in Oreochromis niloticus fish inhabiting the River Nile water.

Histological and parasitic examinations represent an excellent tool for determining the effect of Nile water contamination on fish health status. This paper outlines the effect of sewage, industrial and agricultural drainage water into six selected sites (El-Kanater El-Khyria, El-Qatta, Tamalay, Kafr El-Zayat, Benha and Talkha) at River Nile branches on Oreochromis niloticus fish. Water samples were collected from the investigated area during winter 2019 to assess the status of water by determining some physicochemical characteristics (temperature, electrical conductivity, transparency, pH, oxygen and nutrient salts). Also, histological features for some organs (liver, kidney, and gills) and parasitic infestation in fish were studied. The results revealed that the uncontrolled inputs of wastes altered the water quality of investigated areas leads to the prevalence of the most common parasites genera which infected Oreochromis niloticus health status in the form of histological alteration.

Common Knowledge and Sequential Team Problems

We consider a general sequential team problem based on Witsenhausen's intrinsic model. Our formulation encompasses all teams in which the uncontrolled inputs can be viewed as random variables on a finite probability space, the number of control inputs/decisions is finite and the decisions take values in finite spaces. We define the concept of common knowledge in such teams and use it to construct a sequential decomposition of the problem of optimizing the team strategy profile. If the information structure is classical, our common knowledge based decomposition is identical to classical dynamic program. If the information structure is such that the common knowledge is trivial, our decomposition is similar in spirit to Witsenhausen's standard form based decomposition [17] . In this case, the sequential decomposition is essentially a sequential reformulation of the strategy optimization problem and appears to have limited value. For information structures with nontrivial common knowledge, our sequential decomposition differs from Witsenhausen's standard form based decomposition because of its dependence on common knowledge. Our common knowledge based approach generalizes the common information based methods of [12] – [14] .

Rainfall and its distribution influences on rain-fed saffron yield and economic analysis

Spatial and temporal variability of precipitation events and rainfall depth are the major uncontrolled inputs in planting rain-fed crops and the prediction of their annual yield. Saffron (Crocus sativus L.) is a strategic crop in Iran and due to its low water requirement and high income is considered in sustainable agricultural strategies. This study was carried out using a dataset from an 8-year rain-fed saffron experiment conducted in Bajgah region (Fars province), Iran. The objective was to determine the effects of annual rainfall, its distribution, and air temperature on saffron yield, and to determine the critical rainfall periods for rain-fed saffron yield. Also, economic analysis was considered for the saffron yield in the study region. Results showed that saffron yield cannot be predicted with adequate accuracy by the annual rainfall. Instead, fall and winter seasonal indices and annual index can be used to predict the rain-fed saffron yield when combined with saffron age and mean maximum daily air temperature in the given season. Results also indicated that rain-fed saffron yield is reversely related to the maximum daily air temperature in winter since warm winter causes the leaves to wither earlier and shortens the vegetative growth period. Internal rates of return for interest rates of 4, 8, and 12% were 30.9, 56.7, and 95.9% respectively which is economically feasible for rain-fed saffron production in the study area.

Regression Tree Control of Multidimensional Static Object

In this paper the control problem of a static system under incomplete information is discussed. A nonparametric control algorithm based on Classification and Regression Tree is proposed and evaluated for different task formulations: controlled inputs – one-dimensional output, controlled and observed uncontrolled inputs – one-dimensional output, controlled and observed uncontrolled inputs – multi-dimensional outputs.

Tracing particulate matter and associated microorganisms in freshwaters

Sediment resuspension represents a key process in all natural aquatic systems, owing to its role in nutrient cycling and transport of potential contaminants. Although suspended solids are generally accepted as an important quality parameter, current monitoring programs cover quantitative aspects only. Established methodologies do not provide information on origin, fate, and risks associated with uncontrolled inputs of solids in waters. Here we discuss the analytical approaches to assess the occurrence and ecological relevance of resuspended particulate matter in freshwaters, with a focus on the dynamics of associated contaminants and microorganisms. Triggered by the identification of specific physical–chemical traits and community structure of particle-associated microorganisms, recent findings suggest that a quantitative determination of microorganisms can be reasonably used to trace the origin of particulate matter by means of nucleic acid-based assays in different aquatic systems.

NONPARAMETRIC IDENTIFICATION AND DUAL CONTROL OF DYNAMIC PROCESSES

The problem of modelling of a discrete-continuous dynamic process with lack of current control values is discussed with the formulation of identifi cation problem. A nonparametric model of dynamic object and nonparametric identifi cation calculation results are given due to incomplete information about control actions. A dual scheme of dynamic object control system is submitted in order to include uncontrolled inputs. The internal control frame includes the unit with a typical control mode. The upper control frame involves a nonparametric regulator which has self-training and dual sense properties. A nonparametric algorithm of dual control is described and its dual nature is analyzed. Comparative calculation results of the dynamic object control system with dual scheme and with PI-controller are submitted.

Rain-fed fig yield as affected by rainfall distribution

Variable annual rainfall and its uneven distribution are the major uncontrolled inputs in rain-fed fig production and possibly the main cause of yield fluctuation in Istahban region of Fars Province, I.R. of Iran. This introduces a considerable risk in rain-fed fig production. The objective of this study was to find relationships between seasonal rainfall distribution and rain-fed fig production in Istahban region to determine the critical rainfall periods for rain-fed fig production and supplementary irrigation water application. Further, economic analysis for rain-fed fig production was considered in this region to control the risk of production. It is concluded that the monthly, seasonal and annual rainfall indices are able to show the effects of rainfall and its distribution on the rain-fed fig yield. Fig yield with frequent occurrence of 80 % is 374 kg ha−1. The internal rates of return for interest rate of 4, 8 and 12 % are 21, 58 and 146 %, respectively, that are economically feasible. It is concluded that the rainfall in spring especially in April and in December has negatively affected fig yield due to its interference with the life cycle of Blastophaga bees for pollination. Further, it is concluded that when the rainfall is limited, supplementary irrigation can be scheduled in March.

Changing water, phosphorus and nitrogen budgets for Valle de Bravo reservoir, water supply for Mexico City Metropolitan Area

Ramírez-Zierold JA, Merino-Ibarra M, Monroy-Ríos E, Olson M, Castillo FS, Gallegos ME, Vilaclara G. 2010. 2010. Changing water, phosphorus and nitrogen budgets for Valle de Bravo reservoir, water supply for Mexico City Metropolitan Area. Lake Reserv. Manage. 26:23-34. Valle de Bravo reservoir (VB) provides water supply to the Mexico City Metropolitan Area and other surrounding cities. Nutrient loading to this reservoir increased 276% for phosphorus (P) and 203% for nitrogen (N) in a single decade. During 2002–2005, P and N mean loadings to VB were 120.8 × 103 kg P/y and 591.8 × 103 kg N/y. These loadings were quite variable because of source variations from uncontrolled domestic and agricultural inputs. More than half (56%) of the maximal water storage of the reservoir was withdrawn annually. Water withdrawal removed 22% of the P input. Comparative examination of P and N mass balances showed that most (85%) of the P input to VB accumulates in the sediments. Despite the hypolimnetic anoxia that VB exhibits from March to October, net P accumulation in the sediments is normally observed. Our results confirm that although VB behaves as a warm monomictic water body, its mean hypolimnetic temperature increases throughout the stratification period. Nitrogen limitation was indicated by the dissolved inorganic nitrogen (DIN) to total dissolved P (DIN:TDP = 8.3, molar) ratio during stratification periods. We estimated that N2 fixation exceeded denitrification. This net fixation could double the N loading from rivers and sewage. Management recommendations include (a) gauging of river and sewage inputs to VB, (b) reduction of P input through treatment of sewage from VB town and (c) monitoring and regulating fertilizers and other nonpoint source inputs in the Amanalco watershed. Valle de Bravo reservoir is similar to other eutrophic tropical lakes and reservoirs that could also improve their water quality through these management practices.

JEM Spotlight: Recent advances in analysis of pharmaceuticals in the aquatic environment

Both ecosystem and human health rely on clean, abundant supplies of water, thus many classes of potential pollutants are regulated. In recent years, the possible risks associated with largely uncontrolled inputs of pharmaceuticals to rivers, lakes, groundwater, and coastal waters, mainly via wastewater, have been a focus of much research. During this time, our capacity to sequester, identify, and quantify pharmaceuticals in environmental matrices has improved. Devices have emerged to allow passive uptake of drugs to augment or replace laborious grab sampling. Advances in sample preparation have streamlined extraction procedures and removed interfering matrix components. New instrumental techniques have allowed faster, more accurate and sensitive detection of drugs in water samples. This review highlights all of these advances, from sample collection to instrumental analysis, which will continue to help us better understand the fate and effects of pharmaceuticals in aquatic systems.

Control of periodically operated reactors

Control of periodically operated reactors has in common with control of reactors operating at steady state the objective of minimizing the effect of disturbances on an objective function such as the cost of a product or the deviation of an outlet concentration of a pollutant from a statuary target. Simple feedback control employing feedback PID regulators, however, is not “adequate for most disturbances because of problems with tracking a time-varying output and the necessarily non-linear character of the reactors with respect to controlled as well as uncontrolled inputs. This contribution is a review of the literature and a discussion of research needs. The literature on the control of periodically operated reactors is not voluminous. Nevertheless this literature clearly indicates that model based predictive controllers can be used for this type of reactor”. Further research on the limitations, maintenance and implementation costs of model based controllers in this application would be worthwhile. Experimental studies are wholly absent. Unique regulators for other periodic operations, such as adaptive control or forcing the output toward a reference trajectory using an open loop model based control strategy, certainly warrant study of their application to periodically operated reactors. Additionally, proper design of the reactor may lead to configurations that are simpler to control and that may not require complex control strategies for efficient operation.