Unwanted Deviations Research Articles

Distribution of Rain Intensity: Daily Maximum Rainfall Data in The Province of South Sumatera and West Sumatera

The high intensity of rain in May 2024 caused flooding in the Sumatra region, especially Padang City and Palembang City. The region of West Sumatra is also known as the ethalae of disasters with frequent earthquakes, floods, and landslides. In Indonesia, many frequency analyses are conducted using the Gumbel distribution without testing data and clear hydrological reasons. It is feared that this method is considered as a routine way, thus causing the risk of unwanted deviations, resulting in many disasters and errors in the planning of water buildings. This research aims to determine and know the type of distribution that is representative for the maximum daily rainfall frequency in West Sumatra and South Sumatra Provinces as a reference for development and disaster mitigation in the region. The research location is in West Sumatra and South Sumatra. The data used for this study were taken from daily maximum rainfall data from rain stations for 35 to 40 years from 24 rain stations in West Sumatra Province, and 16 rain stations for South Sumatra region. This type of research is development research with the 4D development model (Define, Design, Develop, Disseminate). The development method consists of four different phases: Initial Investigation, Design, Realisation and Development. The data of each station was then organised into two types of data series, namely the annual maximum data series and the annual minimum data series. The results of this data series test are expected to follow one or more types of distributions commonly used in Hydrology and Drainage. The distribution results obtained for the South Sumatra region are Gama-III and LP-III, while for the West Sumatra region the distribution types are Gama-III, LP-III Normal Log and Gembel. The conclusion obtained is that West Sumatra does not have only one type of distribution. For the type of test using the SPSS.22 application, the results obtained are goodness of fit test, parametric test, Chi-Square test, Kolmogorov Smirnov test and Anderson-Darling test and Histrogram (visual) method.

PDR: Progressive Depth Regularization for Monocular 3D Object Detection

Accurately predicting object depth is a key challenge in monocular 3D detection task. The perspective projection principle used by most state-of-the-art approaches demands a complex balance between the ratio-form depth estimation and 2D-3D geometric regularizations, and thus can lead to sub-optimal solutions. In this paper, we propose a novel synergistic scheme that can achieve better trade-off among these competing objectives. Our main proposal is a progressive depth regularization (PDR) architecture that splits the overall training process into three sequential depth estimation steps to gradually remove the unwanted deviations induced by the over-regularization. Specifically, our model first learns the coarse depth with the conventional perspective projection and combines the coarse-to-fine generation to reduce the search space of 2D projection height prediction. We then deactivate individual supervision on 2D projection height prediction and introduces a new auxiliary 3D physical height prediction to relax the 2D and 3D regularizations, respectively. Consequently, our PDR leads to more precise depth estimation by mitigating the inherent ambiguities in the geometric priors of perspective projection through progressive regularization relaxation. Extensive experiments on both KITTI and Rope3D benchmark show that our PDR delivers strong performance gains as compared to the previous methods.

THE EFFECT OF HYBRIDIZATION ON THE GOVERNANCE OF A PUBLIC-PRIVATE PARTNERSHIP (PPP) IN THE CONSPECTUS OF MANDATORY AND VOLUNTARY FINANCIAL REPORTING

In the conceptualization and theoretical framework of hybrid organizations and in the construction of a role of key performance indicators, Alter (2004), and Ponte et al. (2017) refer to hybrid organizations as the result of hybridization, between the entities with and without profit. These hybrid organizations operate on the border between the non-profit (or public) and for-profit (or private) sectors and are both market-oriented and mission--oriented. The research project presented in this work shows how a hybrid organization faces the problem/challenge of managing, measuring, and reporting financial information, as well as the tools it uses to prevent possible deviations from mission or revenue. Through a systematic review of the literature, the author intended to contribute to the identification and characterization of performance measurement tools, which allow the control of both objectives, social and financial performance, in the hybrid organizational context, to reduce the risk of unwanted deviations. The performance of a PPP is measured from at least three perspectives: (1) the private aspects, such as a return of investment, (2) the public aspects, such as improvements in quality of life, and (3) the partnership itself, as an organization and management capacity. As a hybrid organization, the company cannot simply look for financial performance, social responsibility issues are also at stake, as well as a set of non-market aspects, such as: ethical behavior, contribution to social development, as well as improving the quality of life of the workforce and their families (Ponte et al 2017). Bearing in mind that performance management is only as effective as its measurements (Grossman, 2012), the measurement tool proposed matches the strategic challenges and control needs of hybrid organizations. The identified model, SBSC, provides a better understanding of the company’s sustainability strategy, inside and outside, associated with a holistic view of the aspects of social responsibility, as well, tracking its impact on the other key performance factors.

IMPROVING THE SYSTEM OF ADAPTIVE MANAGEMENT OF AGRICULTURAL ENTERPRISES ON THE BASIS OF CONTROLLING

The article is devoted to solving the problem of improving the efficiency of the adaptive management system of an agricultural enterprise on the basis of controlling. The necessity of introducing a management system aimed at ensuring a phased management of enterprise processes, taking into account the specifics of functioning with a high level of efficiency, flexibility and efficiency, has been substantiated. A retrospective analysis of the formation and development of the control system in industrialized countries has bee carried out. The results of the analysis made it possible to highlight the main concepts of controlling in accordance with their orientation. The peculiarities of the organization of the controlling system at agricultural enterprises of Ukraine in the conditions of seasonal market fluctuations have beenare determined. Growing crops, unlike the production of products in other industries, has such a feature as seasonality. The seasonal nature of production of agricultural enterprises determines the parameters of the activities of industries that produce and maintain agricultural products, harvest, preserve, process and sell agricultural products. Seasonal fluctuations have a direct impact on the intensity of the use of financial, material and technical, labor, energy and other types of resources of agricultural enterprises in certain periods of time during the calendar year. Thus, they impede the effective planning of the financial and economic activities of agricultural enterprises. This requires the improvement of the management system of an agricultural enterprise based on controlling to balance financial flows between all links of the agroindustrial complex. A mathematical model describing time parameters has been presented. Within the framework of the presented model, controlling actions aimed at ensuring the basic conditions for the functioning of an agricultural enterprise and preventing the phenomenon of shortage of funds during the “low” market period have been proposed. The basis for the construction of the model is the structure and objective proportions that determine the ratio between monetary funds and flows of funds of the enterprise of certain periods of its production and sales activities in the short term. The model allows timely detection of problems and making appropriate corrections in management decisions in order to minimize the destabilizing influence of environmental factors and eliminate unwanted deviations. Controlling actions are formed in the form of reports for the purpose of further use in the process of implementing the developed business processes.

A semi-desirable location and sizing model for hydrogen fuel storage areas: A case study for Istanbul

Due to their sustainable nature and high energy efficiency, hydrogen fuel vehicles (HFVs) gradually replace gasoline internal combustion engine vehicles. The increasing number of HFVs brings out the need for installing hydrogen fuel storage areas (HFSAs) that serve as local suppliers for the fuel retail stations. In this study, we consider the HFSA locating and sizing problem for the Anatolian side of Istanbul and pose it as a bi-objective semi-desirable facility location problem which incorporates both social and transshipment costs as push and pull objectives. Next, by adopting a weighted goal programming framework, we minimize the total weighted unwanted deviations from each goal for years between 2021 and 2030 while accounting for several organizational and governmental rules. Our modelling approach is also supported by a structured demand prediction methodology which accounts for the HFV market penetration rates and other parameters such as predicted population, number of HFVs on road, etc. Having two conflicting objectives, we adopt a posteriori approach and generate a representative set of non-dominated solutions to provide decision support to planners and investors. Finally, we perform a sensitivity analysis to account for the uncertainty in the hydrogen fuel demands of districts by defining an uncertainty set for the demand of each district. Sensitivity analysis reveals how and how much change in the district demands modify the most preferred solution.

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equations to have an imperfect solution. In addition, in the case of highly underdetermined parameterization, noise can be absorbed by the model, generating spurious solutions. This is a very undesirable situation that might lead to incorrect conclusions. We presented mathematical formalism based on the inverse problem theory combined with artificial intelligence methodologies to perform an enhanced sampling of noisy biomedical data to improve the finding of meaningful solutions. Random sampling methods fail for high-dimensional biomedical problems. Sampling methods such as smart model parameterizations, forward surrogates, and parallel computing are better suited for such problems. We applied these methods to several important biomedical problems, such as phenotype prediction and a problem related to predicting the effects of protein mutations, i.e., if a given single residue mutation is neutral or deleterious, causing a disease. We also applied these methods to de novo drug discovery and drug repositioning (repurposing) through the enhanced exploration of huge chemical space. The purpose of these novel methods that address the problem of noise and uncertainty in biomedical data is to find new therapeutic solutions, perform drug repurposing, and accelerate and optimize drug discovery, thus reestablishing homeostasis. Finding the right target, the right compound, and the right patient are the three bottlenecks to running successful clinical trials from the correct analysis of preclinical models. Artificial intelligence can provide a solution to these problems, considering that the character of the data restricts the quality of the prediction, as in any modeling procedure in data analysis. The use of simple and plain methodologies is crucial to tackling these important and challenging problems, particularly drug repositioning/repurposing in rare diseases.

Handling Cash In And Cash Out On Dimas Mart Supermarket Kampar Regency

In general, every company, both small and large, must have Cash In and Cash Out. Cash has a central position in maintaining smooth operations. Cash also has the nature that its ownership cannot be proven and is easily transferred, so it is easy to misappropriate or embezzle. Therefore, it is necessary to carry out strict cash supervision, and there must be proper reporting of its use. The company must be careful in carrying out activities related to Cash In and Cash Out, meaning that every cash recipient and cash disbursement transaction must be recorded so that unwanted deviations do not occur. Cash receipts and disbursements must be carried out effectively and efficiently, so there is no budget waste. Therefore, we need an adequate system to be able to monitor cash receipts and cash disbursements. This research aims to explain how to handle cash inflows and outflows and how to handle cash bookkeeping at the Dimas Mart supermarket. So that the supermarket can improve procedures for handling cash inflows, cashouts, and cash bookkeeping so that it can be a reference in conducting similar research at a place and time. Different.

A prescribed performance PID control of robotic cars with only posture measurements considering path curvature

This paper addresses the prescribed performance adaptive neural observer-based PID controller for robotic cars by considering the trajectory curvature compensation. Constrained relative distance and angle errors between a car and leader are transformed into a new Euler-Lagrange model of unconstrained errors via prescribed performance technique. Two filtered PID error variables are introduced to propose a neural adaptive robust observer-based controller which obtains the followings: (i) the prescribed performances of tracking errors are ensured, (ii) the controller singularity is avoided, (iii) the trajectory curvature is compensated to avoid unwanted deviations between the leader and follower, (iv) car velocities measurements are not required, and (v) controller compensates uncertainties by combining neural networks and adaptive robust control. Lyapunov's method proves the controller stability. Comparative simulations verify controller advantages.

Hybrid Uncertainty-Goal Programming Model with Scaled Index for Production Planning Assessment

Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts’ judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution.

DEVELOPMENT OF A MULTI-OBJECTIVE PRODUCTION PLANNING MODEL FOR A SAWMILL

Based on the objectives set out for a Sawmill, a goal programming model was developed to simultaneously consider the production volumes goal, sales revenue goal, production cost goal, and machine utilization goal in order to develop its production plans for a horizon. The unwanted deviations from the goals served as the objective function to be optimized subject to the goals constraints, operational constraints, and non-negativity constraints. Three independent pre-emptive goal priority structures, GP1, GP2, and GP3, were considered with each prioritizing the objectives differently. The goal programming model was tested for its utility using data gathered from the mill to the three-goal priority structures. The results obtained indicated that, for GP1, the product volume goals for all products were achieved, and all but one, volume goals were achieved for both GP2 and GP3. The viability test showed that all priority structures used were profitable with GP1, GP2, and GP3 recording 1.099, 1.102, and 1.095 respectively. This indicates that the three priority structures considered are approximately profitable at the same level. The goal programming model for production planning offers the decision-maker a variety of options as to its application.

Vibration Analyses of an Axial Turbine Wheel With Intentional Mistuning

Abstract The last-stage bladed disk of a steam turbine is analyzed with respect to both flutter susceptibility and limitation of forced response. Due to the lack of variable stator vanes, unfavorable flow conditions may occur, which can lead to flow separation in some circumstances. Consequently, there is the risk of flutter in principle, particularly at nominal speed under part load conditions. For this reason, intentional mistuning is employed by the manufacturer with the objective to prevent any self-excited vibrations. A first step in this direction is done by choosing alternate mistuning, which keeps the manufactural efforts in limits since only two different blade designs are allowed. In this sense, two different series of blades have been made. However, it is well known that small deviations from the design intention are unavoidable due to the manufacturing procedure, which could be proved by bonk tests carried out earlier. The influence of these additional but unwanted deviations is considered in numerical simulations. Moreover, the strong dependence of blade frequencies on the speed is taken into account since centrifugal stiffening effects significantly attenuate the blade-to-blade frequency difference in this particular case. Focusing on the first flap mode, it could be shown that a mitigation of flutter susceptibility is achieved by prescribing alternate mistuning, which indeed evokes an increase of originally small aerodynamic damping ratios. Nevertheless, the occurrence of negative damping ratios could not be completely precluded at part load conditions. That is why optimization studies are conducted based on genetic algorithms with the objective function of maximizing the lowest aerodynamic damping ratios. Again, only two different blade designs are admitted. Finally, mistuning patterns could be identified causing a tremendous increase of aerodynamic damping ratios. The robustness of the solutions found could be proved by superimposing additional random mistuning.

POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

A Guideline to Set Up Cascaded Continuous Cultivation with E. coli Bl21 (DE3).

Continuous processing allows to maximize space-time yields and is implemented in many industrial branches. However, in manufacturing of value added compounds produced with microbial hosts, continuous processing is not state-of-the-art yet. This is because fluctuating productivity causes unwanted process deviations. Cascaded continuous bioprocessing, unlike conventional continuous process modes, was found to result in stable productivity. This manuscript serves as a guideline how to set up a cascaded continuous cultivation with Escherichia coli BL21 DE(3).

Broadening Sight at the Board Level: Perspectives from Error Management Research

A major insight of research into organizational error is that there is a critical period between “hoping for better” and “too little too late”. Engaging in the active management of unfolding organizational errors before they evolve into crises (Pearson & Mitroff, 1993) becomes a strategic concern in itself – one that challenges classic approaches (Edmondson & Verdin, 2018; Giolito & Verdin, 2018). Recent research suggests that the management of organizational errors (e.g. Frese & Keith, 2015; Goodman et al., 2011; Lei, Naveh, & Novikov, 2016) is increasingly a responsibility for top executives, particularly in turbulent contexts where organizations devote increasing time and resources to experimentation. In this session, we turn to the responsibility of corporate boards in strategic error management, defined as the actions taken by organizational actors and that aim to disconnect these errors from strategic failure (Goodman et al., 2011; Hofmann & Frese, 2011; Giolito & Verdin, 2018). Many aspects of strategic failure can only be addressed and corrected by engaging in actions to minimize negative and maximize positive outcomes of the errors as they occur (Frese & Keith, 2015; Hofmann & Frese, 2011a). We assert that it is a core function for organizational actors to engage in and oversee these corrective actions, and that aspects of leadership, governance, and culture play an integral role in the capacity of an organization to respond to errors and avoid crises. Revisiting some of the basic insights of error management, this panel will explore the explicit responsibilities of the board in detecting, recognizing, and dealing with errors in organizations, especially those that affect their long-term success and viability. We will focus on how and when error management takes place and should take place at board level, and what interactive role stakeholders and institutions play in attenuating or magnifying the effects of the board’s behaviors and actions. We look to explore two parallel obligations at board level, both 1) the board’s responsibility for oversight of the error management capability of the organization, and 2) the responsibility for error management of the board’s own activities and functioning. Key questions addressed in this panel will revolve around: What should be the board’s effective functioning related to error management, as distinct from ad hoc crisis management capabilities, risk and compliance, and other frameworks and practices? What role does strategic error management play at the board level with respect to enhancing sustainability, both of the firm itself and of its environment – or even with respect to “planetary boundaries” (Whiteman, Walker & Perego, 2013)? How can error management practice improve the board’s capabilities to offset an organization’s slow drift towards obsolescence or failure? How can we restructure board and governance systems to better “see” the bigger picture, both in terms of major failure and in terms of opportunities missed? How can error management – or a “strategy as learning” perspective (Edmondson & Verdin, 2017, 2018) – help boards catch and address unwanted deviations from strategic goals? Our panel of highly experienced scholars represents several major research streams, as well as organizational and governance practice, with the aim of fostering a rich trans-disciplinary discussion. We plan to reserve ample room for dialogue with the audience, of which practitioners and consultants will hopefully form a significant part.

Models of Frequency Characteristics of Ecological Systems and their Conversion to Sound Form

Real-time monitoring of the state of ecological systems can contribute to early warning of their deviation from an equilibrium state (homeostasis) or a change that leads to a threat to human health or existence. In addition to the existing means of monitoring the state of ecological systems and models for predicting the assessment of their state in the future, it is proposed to use models of the frequency characteristics of these systems, monitoring of which can detect signals about the appearance of unwanted deviations from homeostasis in the form of a change in the frequency spectrum. A change in the frequency spectrum can be converted into the sound waveform, which will allow timely detection of this undesirable change in the state of the ecological system. As a new information channel and analysis of the dynamics of the state of ecological systems, in the article it is proposed to use the wavelet transform of time series with the subsequent translation of the totality of their harmonic vibrations into sound form. In contrast to the Fourier transform, in which the spectrum of stationary and non-stationary processes is practically indistinguishable and it is impossible to determine the moment of the appearance of a new harmonic, the wavelet transformation gives this opportunity. In addition to the purely utilitarian application of the conversion of the vibrational characteristics of an ecological system into sound form, it becomes be possible to convert them into the “music” of ecological systems, which may give a new direction for creative understanding of the state of nature.

Designing adaptive PID controller non-sensitive to changes in aerodynamic characteristics of an unmanned aerial vehicle

The method of implementing an adaptive PID controller using a reference model of an unmanned aerial vehicle is presented. The unmanned aerial vehicle has a nonlinear characteristic and high sensitivity to external influences. The operation of a standard controller in a nonlinear model in the event of disturbing influences does not meet the specified quality criteria. Problems that affect the flight time of the unmanned aerial vehicle are represented by variations in aerodynamic coefficients in the known ranges. Herewith, aerodynamic parameters change, and the system becomes unstable. To eliminate unwanted deviations, an adaptive PID controller loop is introduced into the aerial vehicle control system. Using the reference model of the control object, the adaptation comparator provides the necessary PID controller settings. The introduction of such a correction control signal allows countering various failures and disturbances that lead to uncontrolled control. It was found that this method of control of the unmanned aerial vehicle is very effective, since the obtained result is closer to the experimental one. The study of failures was carried out through the observation of changes in aerodynamic coefficients. The study of changes in aerodynamic coefficients allows failure-free determination of the nominal values of the object coefficients. Such an approach to modeling the unmanned aerial vehicle also makes it possible to solve the economic side of the problem – to conduct experiments in the ANSYS-CFX aerodynamic application without costs for restoring vehicles and structures lost as a result of experimental testing

THE NECESSITY FOR DIAGNOSTICS USING IN THE SYSTEM OF ANTICRISIS MANAGEMENT OF AGRICULTURAL INDUSTRY ENTERPRISES

THE NECESSITY FOR DIAGNOSTICS USING IN THE SYSTEM OF ANTICRISIS MANAGEMENT OF AGRICULTURAL INDUSTRY ENTERPRISES

Cognitive modeling of knowledge management processes in regions

The tools for managing knowledge elaboration of regions were developed. It was substantiated that the knowledge development of regions is ensured by the accumulation and efficient use of knowledge types of capital. The composition of knowledge types of capital, which include intellectual, human and innovative capital, was proposed. It was established that the optimal ratio of knowledge types of capital, which is influenced by various factors, should be of great importance for the formation of the knowledge economy in the regions. It was proved that it is rational to include the population of the region as a part of the main factors of influence; the total amount of knowledge possessed by the population, enterprises, scientific institutions, higher education institutions and local authorities of the region; the conditions for realization and disclosure of knowledge potential of the population of the region. Cognitive models of dependence of the ratio of volumes of intellectual, human and innovative types of capital depending on the combination of factors influencing the successful formation of the knowledge economy in the region were developed. It was found that in order to organize an effective process of management and timely control over the development of unwanted deviations in the structure of accumulation of knowledge type of capital at the regional level, it is rational to introduce a special remote mechanism of dispatching control aimed at the early detection and prevention of problematic situations in creating and distributing the latest knowledge. The expected tendencies of knowledge development of the regions of Ukraine were defined, and the optimal ratio of knowledge types of capital based on the cognitive modeling were determined.

РОБАСТНОЕ УПРАВЛЕНИЕ БЕСПИЛОТНЫМ ЛЕТАТЕЛЬНЫМ АППАРАТОМ ПРИ НЕОПРЕДЕЛЕННЫХ СКОРОСТЯХ

This paper presents the method for implementing robust control using a nominal model of an unmanned aerial vehicle (UAV). The operation of a classical controller in a nonlinear control system in the event of disturbing influences does not satisfy the specified quality criteria. This changes the aerodynamic parameters, and the system becomes unstable. To eliminate unwanted deviations in the control system of the aircraft introduced robust control. The introduction of such a correction control signal allows you to fend off all sorts of failures and disturbances that lead to uncontrolled control. Changes in the aerodynamic lift coefficients, coefficient of resistance, and moments affect the model of the object. The nominal model is calculated by calculating the coefficients with the ANSYS-CFX software and the calculation is confirmed experimentally. Errors are also modeled by this software, and the ranges of variation of each coefficient are a set of failures.

Robust Design of a Smart Structure under Manufacturing Uncertainty via Nonsmooth PDE-Constrained Optimization

We consider the problem of finding the optimal shape of a force-sensing element which is integrated into a tubular structure. The goal is to make the sensor element sensitive to specific forces and insensitive to other forces. The problem is stated as a PDE-constrained minimization program with both nonconvex objective and nonconvex constraints. The optimization problem depends on uncertain parameters, because the manufacturing process of the structures underlies uncertainty, which causes unwanted deviations in the sensory properties. In order to maintain the desired properties of the sensor element even in the presence of uncertainty, we apply a robust optimization method to solve the uncertain program.The objective and constraint functions are continuous but not differentiable with respect to the uncertain parameters, so that existing methods for robust optimization cannot be applied. Therefore, we consider the nonsmooth robust counterpart formulated in terms of the worst-case functions, and show that subgradients can be computed efficiently. We solve the problem with a BFGS--SQP method for nonsmooth problems recently proposed by Curtis, Mitchell and Overton.