Multivariable Systems Research Articles

Studying Retroviral Life Cycles Using Visible Viruses and Live Cell Imaging.

Viruses exploit key host cell factors to accomplish each individual stage of the viral replication cycle. To understand viral pathogenesis and speed the development of new antiviral strategies, high-resolution visualization of virus-host interactions is needed to define where and when these events occur within cells. Here, we review state-of-the-art live cell imaging techniques for tracking individual stages of viral life cycles, focusing predominantly on retroviruses and especially human immunodeficiency virus type1, which is most extensively studied. We describe how visible viruses can be engineered for live cell imaging and how nonmodified viruses can, in some instances, be tracked and studied indirectly using cell biosensor systems. We summarize the ways in which live cell imaging has been used to dissect the retroviral life cycle. Finally, we discuss select challenges for the future including the need for better labeling strategies, increased resolution, and multivariate systems that will allow for the study of full viral replication cycles.

Nonlinear dynamic inversion based full envelope robust flight control for coaxial compound helicopter

The flight control system of coaxial compound helicopters (CCHs) is a complex multi-variable system characterized by redundant control effectors, strong nonlinear characteristics, and multiple flight modes. This paper presents a unified robust control framework tailored for CCH full-envelope flight, utilizing nonlinear dynamic inversion and extended state observer techniques. The framework is specifically designed to tackle issues arising from a multi-mode nature, parametric uncertainties, and external disturbances. Within the unified framework, the study addresses inherent complexities such as nonlinearities and cross-coupling effects in CCH by leveraging nonlinear dynamic inversion to formulate flight control laws for both inner and outer-loops. To mitigate model uncertainties and external disturbances, the extended state observer is employed to estimate lumped disturbance effectively. Control allocation for both inner and outer-loops is devised to adapt to changes in control effector authorities and flight modes. Furthermore, an exponential control allocation strategy is proposed for the outer-loop to ensure a smooth pitch angle during transition flight. The effectiveness of the proposed control laws and control allocation strategies in achieving precise attitude tracking, transition flight and mission task elements (MTEs), even in the presence of notable model uncertainties and external disturbances, is demonstrated through numerical simulations.

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

ProIDS: A Segmentation and Segregation-based Process-level Intrusion Detection System for Securing Critical Infrastructures

Critical infrastructures (CIs) are highly susceptible to cyber threats due to their crucial role in the nation and society. Intrusion Detection Systems (IDS) are deployed at the process level to enhance CI security. These process-level IDSs are broadly categorized into univariate and multivariate systems. Our research underscores that both types of systems encounter limitations, especially in handling correlations among process variables (PVs). Univariate IDSs neglect correlations by assessing PVs in isolation, while multivariate IDSs capture these but are vulnerable to evasion attacks. In response, we introduce ProIDS- a novel segmentation and segregation-based process-level IDS. ProIDS leverages the inherent correlations among PVs while segregating them into distinct units to enhance security against evolving threats. This strategic approach ensures the capture of correlations and mitigates the risk of evasion attacks, enhancing the system’s ability to detect abnormal activities. Additionally, ProIDS offers non-parametric modeling for heightened performance, minimal computational overhead, and noise reduction properties. Our comprehensive experiments demonstrate ProIDS’s superiority over baseline methods, delivering precise detection of various attacks while maintaining operational efficiency.

Neural algorithm for optimization of multidimensional object controller parameters

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.

Novel Weakness Multivariate Quadratic Structures Detected within Macaulay Matrix

The security of a Multivariate Public-Key Cryptosystem (MPKC) is based on the hard mathematical problem of solving Multivariate Quadratic (MQ) equations over finite fields, also known as the MQ problem. An MPKC has the potential to be a post-quantum cryptosystem. In this paper, we identify new weaknesses in the Macaulay matrix identified via Wang's technique, which was initially designed for solving multivariate quadratic equation systems. This new weakness occurs in the case of random coefficients in any column vector for different variables of monomials and random coefficients are assigned to other monomials. The weakness is exposed through the use of Gaussian elimination to obtain a univariate equation. We illustrate our findings using a random example.

Shared Genetic Liability across Systems of Psychiatric and Physical Illness.

Epidemiological literature has shown that there are extensive comorbidity patterns between psychiatric and physical illness. However, our understanding of the multivariate systems of relationships underlying these patterns is poorly understood. Using Genomic SEM and Genomic E-SEM, an extension for genomic exploratory factor analysis that we introduce and validate, we evaluate the extent to which latent genomic factors from eight domains, encompassing 76 physical outcomes across 1.9 million cases, evince genetic overlap with previously identified psychiatric factors. We find that internalizing, neurodevelopmental, and substance use factors are broadly associated with increased genetic risk sharing across all physical illness domains. Conversely, we find that a compulsive factor is protective against circulatory and metabolic illness, whereas genetic risk sharing between physical illness factors and psychotic/thought disorders was limited. Our results reveal pervasive risk sharing between specific groups of psychiatric and physical conditions and call into question the bifurcation of psychiatric and physical conditions.

Multivariate nonlinear analysis of climatological variables and its effect on local temperature.

Atmospheric temperature describes the specific heat content of the air at particular places and times. In this sense, the presented work proposes a multivariable system that takes data sets of different climatological variables, with the aim of regulating the temperature level. The diversity in climatological variables significantly affects precipitation, humidity, wind speed and temperature. Thus, a study has been carried out on these variables in terms of nonlinear dynamics. The aim of the work is to obtain a better understanding of the dynamics of local climatological variables. On the other hand, due to the fact that the time series analyzed are small, the analysis becomes complex, at the moment of joining all the variables in conjunction, and processing them by means of multivariate statistical prediction methodologies. The results have shown the mean values in the different variables with which a temperature is maintained between and , which shows possible work with multi-objective optimization of the obtained model.

Analysis of the Relationships between Variables and Their Applications in the Energy Saving Field

Energy saving is an important measure to promote social green transformation. The traditional energy-saving ideas usually only focus on a specific loss, and seldom consider the possible relationship and influence among various losses. In relatively complex energy-using systems, there are often many kinds of losses, and each loss may have many influencing factors. There may be some relationship between these losses and the influencing factors. To solve this problem, this paper presents an analysis method of the variable association in multi-variable systems. First, the basic relationships between variables and the representation methods are discussed. The basic concept of a path between variables is given, and the analysis method of variable association based on path statistics is provided. This paper focuses on the analysis of the influencing factors and paths of the observed variables, as well as which observed variables will be affected by a control variable. Then, based on the correlation matrix, the quantitative analysis method of the influence between variables is given. Variable correlation analysis is innovatively applied in the field of energy saving to determine the correlation of losses through variable associations, guiding the preliminary screening of energy-saving measures and analyzing the collateral effects of these measures. Based on the correlations between energy losses, a scientific process for formulating energy-saving measures is proposed. The variable correlation analysis method proposed in this paper is a generalized method, which can judge the correlation between variables from the perspective of theoretical analysis and avoid the dependence on data. In addition to good applications in the field of energy conservation, it can also be widely used in construction, transportation, climate change, and other fields. The proposed energy-saving ideas take into account the intensity of influencing factors on loss and the correlation between loss, which improves the effectiveness of energy saving measures.

Structure modification based PID neural network decoupling control for nonlinear multivariable systems

In this research, a structure modification based PID neural network (PIDNN) decoupling strategy is proposed to solve the difficulty in controller caused by the strong coupling in nonlinear multivariable systems. Incomplete differential neurons are first introduced into the hidden layer of the PIDNN, which achieving a decreased amount of control overshoot and better control stability. Then, an improved integration strategy is incorporated in the hidden layer structure, resulting in expedited convergence speed in the PIDNN control. Furthermore, intelligent optimization algorithms are employed to improve the convergence rate of the proposed PIDNN. The stability of the proposed controller is analyzed, and an adaptive learning rate scheme is derived. Finally, to confirm the effectiveness of the controller, three examples and the analysis of experimental results are provided.

Dual-Loop μ-Synthesis Direct Thrust Control for Turbofan Engines

As the power unit of an aircraft, the engine’s primary task is to provide the demanded thrust, making research on direct thrust control crucial. However, being a complicated multivariable system, effective multivariable direct thrust control methods are currently lacking. The main content of this paper is threefold. First, it presents a dual-loop multivariable μ-synthesis direct thrust control scheme for mixed-exhaust low-bypass turbofan engines, which is a typical rotationally symmetric machine. The scheme adjusts fuel flow for thrust control and nozzle area to control the turbine pressure ratio, ensuring thrust tracking while maintaining the engine’s key parameters within safe limits. Second, a fast, accurate thrust estimation algorithm based on aerodynamic thermodynamics and component characteristics is introduced. At last, considering the model uncertainties between off-design and design points, a weight function frequency shaping μ-synthesis control design method is proposed to address internal loop coupling and external disturbance suppression. Nonlinear simulations within the flight envelope show that μ-synthesis direct thrust control achieves robust servo tracking and disturbance rejection, with a maximum steady-state thrust error of no more than 0.1%, and the key parameters are not over their safety boundaries.

Optimal Controller Identification for multivariable non-minimum phase systems

This work deals with data-driven control for non-minimum phase (NMP) systems, where the goal is to design a controller for a plant whose model is unknown by using a batch of input–output data collected from it. The approach is based on the Model Reference paradigm, where a transfer function matrix – the reference model – is used to specify the desired closed-loop performance. The NMP systems issue in Model Reference approaches is a well-known problem in control literature and it is no different in data-driven methods. This work explains how to adapt the formulation of the Optimal Controller Identification (OCI) method to cope with this class of systems. Considering a convenient parametrization of the reference model and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.

Nonlinear Multivariable System Identification: A Novel Method Integrating T-S Identification and Multidimensional Membership Functions

In this paper, a new multidimensional Takagi–Sugeno (T-S) identification technique is proposed for multivariable nonlinear systems. In this technique, multidimensional membership functions are designed using concepts from solid mechanics. The design of membership functions is carried out in multidimensional space, defining the principal axes from the eigenvectors of the inertia matrix, and it has the characteristic of dividing the space into regions with the same inertia. These regions are analyzed to define the center of gravity for each rule. Illustrative examples of multivariable nonlinear systems, such as a thermal mixing process and a binary distillation column, are selected to evaluate the effectiveness of the proposed method. The proposed method is compared with traditional T-S identification that uses one-dimensional membership functions and shows a reduction in the relative identification error and the algorithm execution time. Additionally, the proposed method prevents rules from being positioned outside the system’s range, thereby avoiding the generation of unnecessary rules.

Path-Sampling and Machine Learning for Rare Abnormal Events: Application to Polymerization CSTRs

Previously, we developed novel, unidirectional, static, multivariate, alarm systems for rare un-postulated abnormal events, demonstrated successfully for an exothermic CSTR. Herein, our techniques are improved significantly for a more-complex polystyrene CSTR, operating in its unstable region, capable of abnormal shifts to two undesirable regions; i.e., unsafe and unreliable regions. BG-FFS, a path-sampling algorithm, is utilized to simulate efficiently multiple rare abnormal trajectories; then, the XGBoost machine learning algorithm is utilized to develop accurate predictive models for committer probabilities; i.e., pB as a function of key process variables – such models, when deployed in real-time, result in improved bidirectional dynamic multivariate alarm systems, capable of response actions using real-time pB predictions. Then, using our rationalization strategies, the initial alarm systems are evaluated and modified, followed by DRAn (Dynamic Risk Analysis) studies and sensitivity analyses to investigate the effects of varying other process parameters to achieve more effective response actions.

Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis

BackgroundThis systematic review aims to assist clinical decision-making in selecting appropriate preoperative prediction methods for difficult tracheal intubation by identifying and synthesizing literature on these methods in adult patients undergoing all types of surgery.MethodsA systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran’s-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks’ funnel plot.ResultsOut of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson’s risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design.ConclusionNo single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.

Global adaptive stabilization for a class of uncertain multivariable systems with nonlinear parametrization

Global adaptive stabilization for a class of uncertain multivariable systems with nonlinear parametrization

Limit hypersurface state of the art Gaidai multivariate risk evaluation approach for offshore Jacket

Current study presents state of the art approach to risk and reliability assessment for multivariate nonlinear dynamic systems. Specifically, novel hypersurface Gaidai risks evaluation methodology has been presented for evaluating offshore structural risks. Advocated approach being particularly suitable for offshore engineering multidimensional dynamic systems, possessing large number of critical components, that have either been physically observed/measured, or numerically modeled over a representative timelapse. Advocated Gaidai hypersurface structural risks evaluation methodology is applicable for a wide range of engineering and industrial systems. This study demonstrates that given in situ environmental conditions, it is well possible to assess accurately risks of system’s failures, damages or hazards, caused by excessive structural dynamics. Multivariate dynamic systems, possessing nonlinear cross-correlations between critical system’s components often present design challenges, when utilizing classic structural risks evaluation approaches, as those are mostly only univariate or bivariate. Dynamic offshore Jacket hot spot stresses have been utilized as an example in this reliability investigation. Modeling system’s excessive dynamics is challenging because of the non-stationarity of the system and the intricacy of fluid-structural dynamic interactions, arising from in situ wave-induced loads, influencing Jacket’s structural dynamics. Nonlinearities greatly affect structural dynamics, e.g., 2nd, 3rd, higher order effects within fluid-structural interactions. Methodology presented in this work offers a straightforward, effective, yet precise means of assessing the risks of failure or hazard for multivariate, non-stationary, non-linear dynamic offshore systems. For bivariate case, verification note has been added.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Influences of thioalcohols on the release of aromas in sesame-flavor baijiu

This study investigated the interactions between 2-furylmethanethiol, benzenemethanethiol, and 18 skeletal aroma-active compounds as well as four aroma notes in sesame-flavor baijiu based on the Feller Additive Model, the Odor Activity Value (OAV) Approach, and the Sigma-Tau (σ-τ) plots. In addition, a predictive model for the interactions between 2-furylmethanethiol and esters was developed, and the determinants of the interaction results in complex systems were explored. The results reveal that both thioalcohols interacted with the skeletal aroma-active compounds in a similar trend, where 2-furylmethanethiol tends to enhance the release of fruit and acid aroma. Moreover, the intensity of the thiols and their intensity ratio to the notes were the determinants of the interaction results in the multivariate blended system, with the lower the concentration of the thiols, the closer the ratio was to 1, and the more likely that additive interactions would take place. Predictive modeling showed that 2-furylmethanethiols were more likely to have additive or synergistic effects with esters when the olfactory thresholds of the esters were between 75.86 and 199.53 μg/L. Conversely, masking effects were more likely.

Noteworthy synthesis strategies and applications of metal-organic frameworks for the removal of emerging water pollutants from aqueous environment

17 global Sustainable Development Goals (SDGs) were established through the adoption of the 2030 Agenda for Sustainable Development by all United Nations members. Clean water and sanitation (SDG 6) and industry, innovation, and infrastructure (SDG 9) are the SDGs focus of this work. Of late, various new companies delivering metal-organic frameworks (MOFs) have blossomed and moved the field of adsorption utilizing MOFs to another stage. Inside this unique circumstance, this article aims to catch recent advancements in the field of MOFs and the utilizations of MOFs relate to the expulsion of arising contaminations that present huge difficulties to water quality because of their steadiness and possible damage to environments and human wellbeing. Customary water treatment techniques regularly neglect to eliminate these poisons, requiring the advancement of novel methodologies. This study overviews engineering techniques for controlling MOF characteristics for better flexibility, stability, and surface area. A current report on MOFs gathered new perspectives that are amicably discussed in emergent technologies and extreme applications towards environmental sectors. Various applications in many fields that exploit MOFs are being fostered, including gas storage, fluid separation, adsorbents, catalysis, medication delivery, and sensor utilizations. The surface area of a wide range of MOFs ranges from 103 to 104 m2/g, which exceeds the standard permeability of several material designs. MOFs with extremely durable porosity are more significant in their assortment and variety than other classes of porous materials. The work outlines the difficulties encountered in the synthesis steps and suggests ways to make use of MOFs' value in a variety of contexts. This caters to creating multivariate systems enclosed with numerous functionalities, leading to the synthesis of MOFs that offer a synergistic blend of in-built properties and exclusive applications. Additionally, the MOF-related future development opportunities and challenges are discussed.