Past Output Research Articles

Influence Of Principals’ Supportive Leadership Style On Tutors’ Support For Implementation Of Performance Appraisal Of Tutors In Public Primary Teachers Training Colleges In Kenya

The study objectives sought; to establish the influence of principals’ supportive leadership style on tutors’ support for implementation of performance appraisal in public primary teachers training colleges in Kenya. The study employed descriptive research design. The target population comprised of 840 tutors and 21 principals drawn from the 21 public teachers training colleges in Kenya as of 2021. Stratified proportionate random sampling was used to select 271 tutors in teachers’ training colleges in Kenya. Census was used to select all the 21 principals of training colleges in Kenya. Primary data were collected using questionnaire for tutors and interview guide for the 21 Principals. The study findings demonstrated that supportive leadership style of principals showed a significant positive effect on tutors' support for performance appraisal (β = 0.386, p < 0.001). The study concluded that principals' leadership styles supportive is critical determinants in enhancing tutors' engagement and commitment to the appraisal process. There is need for the college management to provide the necessary teaching and training materials to support the effective implementation of performance appraisal programmes. There is also need for tutors to present their past output so that it can inform supportive principal what is necessary for a better working environment. The study also recommends that the MoE, alongside college management, prioritize the creation of policies that promote a supportive and safe working environment. This could include mentorship programs for tutors, flexible scheduling, and recognition and reward systems to acknowledge tutors’ professional growth and accomplishments

Ice-marginal volcanic sequence in Iceland found on a nondescript gradual hillslope: An unexpected record of ice thickness late in deglaciation

Volcanism increases when glaciers melt because isostatic rebound during deglaciation decreases the pressure on the mantle, which enhances decompression melting. Anthropogenic climate change is now causing ice sheets and valley glaciers to melt around the world and this deglaciation could stimulate volcanic activity and associated hazards in Iceland, Antarctica, Alaska, and Patagonia. However, current model predictions for volcanic activity associated with anthropogenic deglaciation in Iceland are poorly constrained, in part due to uncertainties in past volcanic output over time compared to ice sheet arrangements. Further work specifically characterizing glaciovolcanic and ice-marginal volcanoes in Iceland is needed to reconstruct volcanic output during time periods with changing ice cover. Here, we describe a previously unrecognized ice-marginal volcanic sequence on a broad, gradual hillslope southeast of Langjökull and the Jarlhettur volcanic chain in Iceland's Western Volcanic Zone. Although previously mapped as interglacial lavas, canyons in this area revealed two southwest-dipping sequences of pillow-bearing tuff-breccias between pāhoehoe lava flows above modern lake Sandvatn. These pillow-bearing tuff-breccias and the quenched meter-scale cavities in coherent lava and cube-jointed facies show lavas came into contact with ice and pockets of trapped meltwater. However, clasts within the tuff-breccias include a mixture of pillow lavas and pāhoehoe fragments, requiring that the subaqueous tuff-breccia facies were derived from subaerial flows. In addition, we observed interfingering of subaerial and transitional subaqueous-subaerial pāhoehoe lava flows with the pillow-bearing tuff-breccias. We propose that during a deglaciation, subaerial lavas sourced upslope from near Skálpanes flowed downslope to the south and came into contact with thin ice north of the modern lake Sandvatn. We constrain the local ice at this time to be ∼30–50 m thick. Importantly, this finding demonstrates that ice-marginal deposits that can provide paleo-environmental constraints may be hidden in terrains without morphologically distinct glaciovolcanic edifices.

Observer-based output feedback control for switched T–S fuzzy systems with local nonlinear segments by using past output measurements

This paper researches the observer-based exponential stabilization problem for switched Takagi–Sugeno (T–S) fuzzy systems with unmeasurable premise variables and local nonlinearities. The local nonlinear parts that satisfy the incremental quadratic constraint exist in the systems and observers, which have the merit of being able to contain many common nonlinearities in a unified framework. Because the premise variables are not measurable, the performance of the observers in estimation is critical. Different from existing observer-based control strategies, the strategy of this paper not only depends on the current outputs of the systems but also relates to the past outputs of the systems which can provide a way to improve performance. In order to tackle the difficulties caused by introducing the past outputs and design a set of non-fragile fuzzy controllers to stabilize the systems, a new augmented state vector is constructed. Then, the conditions for designing the observer-based controller are derived by using auxiliary scalars, projection Lemma and S-procedure. Finally, the effectiveness of the control strategy obtained in this paper is proved by two examples.

Trends in Rhinoplasty Research in Saudi Arabia: A 30-Year Bibliometric Analysis.

Rhinoplasty is one of the most commonly performed aesthetic surgeries. Saudi Arabia has a large number of rhinoplasty publications. Here, we aimed to assess the past, present, and future research output related to rhinoplasty in Saudi Arabia and compare it with global output. We performed a bibliometric analysis to evaluate rhinoplasty research productivity trends in Saudi Arabia from 1995 to 2021 in both cosmetic and reconstructive rhinoplasties. We considered all publications whose first authors were from Saudi Arabia or whose authors contributed significantly to a paper from Saudi Arabia, even though the first author was not from the Kingdom of Saudi Arabia. We evaluated the research quality of the articles using the impact factor of the journal in which the article was published. For each article, the number of authors, number of citations received, study design, region of the first author, and the level of evidence were collected. We gained several insights into the global trends of rhinoplasty in research and its position. We observed a significant increase in the number of publications on rhinoplasty in Saudi Arabia. Although there was no significant increase in the impact factor, most publications had a level of evidence of I or II.

Dynamics of a discrete-time mixed oligopoly Cournot-type model with three time delays

This paper analyzes a discrete-time Cournot competition model with time delays taking into consideration the dynamics of market interactions involving one public firm and multiple private firms. The model assumes that the production output of the public firm is influenced by the past output levels of the private firms. The output levels of the private firms are affected by the past production outputs of both the public firm and their private competitors. The study identifies two equilibrium states within the nonlinear system: a positive equilibrium and a boundary equilibrium. Through a stability analysis, it is established that the boundary equilibrium is a saddle point. In the absence of time delays, the stability region for the positive equilibrium is determined. The research explores various specific delay scenarios, finding conditions under which the positive equilibrium is asymptotic stability. Additionally, the occurrence of flip and Neimark–Sacker bifurcations are examined. In order to illustrate the complex dynamics, the paper provides a series of numerical examples.

Exchange Rate Depreciation and the Nigeria’s Agricultural and Industrial Sectors

This study examines the effect of exchange rate depreciation on the agricultural and industrial sectors of the Nigerian economy using annual data from 1981 to 2021 in an autoregressive distributed lag (ARDL) model. The findings show that the effect of exchange rate depreciation on industrial and agricultural output was fairly similar. Exchange rate depreciation, inflation, interest rate, and government expenditure have negative effects on industrial output in the short run and positive effects in the long run. Similarly, past agricultural output, exchange rate depreciation, and government expenditure have negative effects on agricultural output in the short run and a positive effect in the long run. Inflation also has a long-term positive effect on agricultural output. Government expenditure had the most substantial long-run effect on industrial and agricultural output. The study shows the need to manage high exchange rate volatility to facilitate realistic forecasts and sound production decisions in the agricultural and industrial sectors. The findings of this study underscore the importance of considering both short-run dynamics and long-run adjustments in understanding the effects of exchange rate depreciation and other economic variables on the industrial and agricultural sectors.

Finite-time filtering design with past output measurements for interval type-2 fuzzy systems: A descriptor approach

This paper is interested in the design of full-order H∞ memory filters for discrete-time nonlinear systems within finite-time domain. First, the investigated nonlinear systems are modeled by a Takagi–Sugeno fuzzy technique for the interval type-2 case. And to reduce the size of the model, only the past measurements of systems are involved during the memory filters construction. Then, novel design processes with less conservatism are proposed through executing a decoupling transformation for the filtering error dynamics. Based on Lyapunov methods together with several strategies that apply convex optimization techniques and slack variables, some sufficient conditions are presented and certified to solve the memory filter parameters, such that the augmented error dynamics are singular finite-time bounded with prescribed H∞ performance. Finally, an example is introduced to illustrate the effectiveness of the proposed strategy in contrast to existent works.

What Observables Are Needed for Precision Data-Enabled Learning of Inverse Operators?

Abstract The advent of easy access to large amount of data has sparked interest in directly developing the relationships between input and output of dynamic systems. A challenge is that in addition to the applied input and the measured output, the dynamics can also depend on hidden states that are not directly measured. In general, it is unclear what type of data, such as past input and or past output is needed, to learn inverse operators (that predict the input needed to track a desired output for control purposes) with a desired precision. The main contribution of this work is to show that, irrespective of the selected model, removing the hidden-state dependence and achieving a desired precision of inverse operators require (i) a sufficiently-long past history of the output and (ii) sufficiently-precise estimates of the output's instantaneous time derivatives that are necessary and sufficient for linear systems, and under some conditions, for nonlinear systems. This insight, about the required observables (output history and derivative) for removing the hidden-state dependence and achieving precision, is used to develop a data-enabled algorithm to learn the inverse operator for multi-input multi-output square systems. Simulation examples are used to illustrate that neural nets (with universal approximation property) can learn the inverse operator with sufficient precision only if the required observables, identified in this work, are included in training.

Influence of Principals’ Supportive Leadership Style on Tutors’ Support for Implementation of Performance Appraisal of Tutors in Public Primary Teachers Training Colleges in Kenya

The study objectives sought; to establish the influence of principals’ supportive leadership style on tutors’ support for implementation of performance appraisal in public primary teachers training colleges in Kenya. The study employed descriptive research design. The target population comprised of 840 tutors and 21 principals drawn from the 21 public teachers training colleges in Kenya as of 2021. Stratified proportionate random sampling was used to select 271 tutors in teachers’ training colleges in Kenya. Census was used to select all the 21 principals of training colleges in Kenya. Primary data were collected using questionnaire for tutors and interview guide for the 21 Principals. The study findings demonstrated that supportive leadership style of principals showed a significant positive effect on tutors’ support for performance appraisal (β = 0.386, p < 0.001). The study concluded that principals’ leadership styles supportive is critical determinants in enhancing tutors’ engagement and commitment to the appraisal process. There is need for the college management to provide the necessary teaching and training materials to support the effective implementation of performance appraisal programmes. There is also need for tutors to present their past output so that it can inform supportive principal what is necessary for a better working environment. The study also recommends that the MoE, alongside college management, prioritize the creation of policies that promote a supportive and safe working environment. This could include mentorship programs for tutors, flexible scheduling, and recognition and reward systems to acknowledge tutors’ professional growth and accomplishments.

Deep learning of vehicle dynamics

Recent advances in deep-learning-based identification of dynamic systems have resulted in a new generation of approaches utilizing state-space neural models with innovation noise structure, improved reformulation of multiple shooting, batch optimization, and a subspace identification-inspired form of encoders. The latter is used to learn a reconstructability map to estimate model states from past inputs and outputs. By using the SUBNET approach, which belongs to the state-of-the-art of these methods, we show how to effectively use these approaches to identify reliable vehicle models from data both in continuous and discrete time, respectively. We showcase the approach on the identification of the dynamics of a Crazyflie 2.1 nano-quadcopter and an F1tenth electric car both in a high-fidelity simulation environment, and in case of the electric car, on real measured data. The results indicate that new-generation of deep-learning methods offer Efficient system identification of vehicle dynamics in practice.

A Multichannel-Based CNN and GRU Method for Short-Term Wind Power Prediction

Incorporating wind energy on a large scale into power systems presents challenges for the operation and control of the grid. To enhance the safety of power grid operation, accurate short-term forecasting of wind power is necessary, as it minimizes the impact of randomness. Considering the uncertainty and prediction issues associated with wind power, this paper introduces a CNN–GRU ultra-short-term wind power prediction model. This model relies on multichannel signals, including data such as wind speed, wind direction, climate conditions, and historical power outputs collected from wind farms. These data types contribute to the formation of a comprehensive multichannel signal for wind power. Following that, the CNN method extracts both global and partial features from these signals. Concurrently, features are extracted from past power outputs based on their time series. These features are then combined with the ones obtained from the convolution process. Subsequently, these combined features are input into a fully connected network. This step is crucial for blending multichannel information and generating forecast results. To validate the model, it was tested using data from a wind farm located in a specific region of Sichuan Province. According to our experimental results, the model demonstrates a high level of accuracy in computation and robust generalization ability.

The Impact of an Effective Tax System On a Developing Economy: A Review of Rwandan and Nigerian Tax Systems

This study analyzes how foreign direct investment (FDI) affected the output of Nigeria's service sector from 1980 to 2020. The study uses service sector output as the regresand, FDI as the major regressor, and exchange rate (EXR) and government spending (GEX) were used as check variables. The data were sourced from the CBN Statistical Bulletin 2020. The nature of the variables necessitated the use of ECM. Results from the short run ECM dynamic model reveals that service sector output responds positively to FDI. We observed that the lag value of service sector output was positive and significant, implying that service sector output responds to past service sector output positively in Nigeria. The ECM's one-period lag coefficient was negative and significant at 0.05, indicating that service sector output adjusts to changes in explanatory variables and its own lag within a year. The ECMt-1 corrects 17.3% of the disequilibrium between static and short-run dynamic models of Service Sector Output (SSO) within a year on average. The study found that the service sector plays a crucial role in this developing economy's economic growth and development. It is an essential macroeconomic sector that can improve the economy's overall performance and influence the flow and direction of income as well as the Balance of Payments (BOP) position. According to the findings, the study suggests that the government should uphold financial responsibility and allocate more funds towards building necessary infrastructure to support the service sector's growth. This will ultimately improve the overall performance of the Nigerian economy.

Foreign Direct Investment and Service Sector Performance in a Developing Economy: Lessons from Nigeria

This study analyzes how foreign direct investment (FDI) affected the output of Nigeria's service sector from 1980 to 2020. The study uses service sector output as the regresand, FDI as the major regressor, and exchange rate (EXR) and government spending (GEX) were used as check variables. The data were sourced from the CBN Statistical Bulletin 2020. The nature of the variables necessitated the use of ECM. Results from the short run ECM dynamic model reveals that service sector output responds positively to FDI. We observed that the lag value of service sector output was positive and significant, implying that service sector output responds to past service sector output positively in Nigeria. The ECM's one-period lag coefficient was negative and significant at 0.05, indicating that service sector output adjusts to changes in explanatory variables and its own lag within a year. The ECMt-1 corrects 17.3% of the disequilibrium between static and short-run dynamic models of Service Sector Output (SSO) within a year on average. The study found that the service sector plays a crucial role in this developing economy's economic growth and development. It is an essential macroeconomic sector that can improve the economy's overall performance and influence the flow and direction of income as well as the Balance of Payments (BOP) position. According to the findings, the study suggests that the government should uphold financial responsibility and allocate more funds towards building necessary infrastructure to support the service sector's growth. This will ultimately improve the overall performance of the Nigerian economy.

DRF codes: Deep SNR-robust feedback codes

We present a new Deep Neural Network (DNN)-based error correction code for fading channels with output feedback, called the Deep SNR-Robust Feedback (DRF) code. At the encoder, parity symbols are generated by a Long Short Term Memory (LSTM) network based on the message, as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bidirectional LSTM architecture along with a Signal to Noise Ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of DNN-based codes over channels with passive output feedback: (i) the SNR-aware attention mechanism at the decoder enables reliable application of the same trained NN over a wide range of SNR values; (ii) curriculum training with batch size scheduling is used to speed up and stabilize training while improving the SNR-robustness of the resulting code. We show that the DRF codes outperform the existing DNN-based codes in terms of both the SNR-robustness and the error rate in an Additive White Gaussian Noise (AWGN) channel with noisy output feedback. In fading channels with perfect phase compensation at the receiver, DRF codes learn to efficiently exploit knowledge of the instantaneous fading amplitude (which is available to the encoder through feedback) to reduce the overhead and complexity associated with channel estimation at the decoder. Finally, we show the effectiveness of DRF codes in multicast channels with feedback, where linear feedback codes are known to be strictly suboptimal. These results show the feasibility of automatic design of new channel codes using DNN-based language models.

A novel H∞ memory observer design for uncertain switched nonlinear systems using past output measurements

AbstractThis paper tackles the problem of robust memory observer design problem for discrete‐time switched nonlinear systems by using past output measurements. Different from traditional design strategies of Luenberger‐type observers using the current output measurements, the memory Luenberger‐type observer design strategy introduces the past output measurements of switched systems and observers simultaneously. In addition, in order to better describe the observer error systems, a novel augmented state variable framework of the observer error systems is developed. The highlight of this paper lies that the memory observer design strategy is developed by considering past output measurements and state error values of memory observers, whose performance can be improved by increasing the number of past output measurements used in the observer design, which is an important discovery and significant novelty of this work. By combining the multiple Lyapunov functions approach and average dwell time technique, and introducing matrix variables with free structure, new sufficient conditions for weighted performance analysis of observer error systems are derived. Then, the memory observer synthesis conditions are obtained in the form of linear matrix inequality (LMI), and the memory observers can be obtained by solving these LMIs. Lastly, two examples are presented to show the advantages and effectiveness of the derived results.

Prediction regions based on dissimilarity functions

This paper presents a new methodology to obtain prediction regions of the output of a dynamical system. The proposed approach uses stored past outputs of the system and it is entirely data-based. Only two hyperparameters are necessary to apply the proposed methodology. These scalars are chosen so that the size of the obtained regions is minimized while fulfilling the desired empirical probability in a validation set. In this paper, methods to optimally estimate both hyperparameters are provided. The provided prediction regions are convex and checking if a given point belongs to a computed prediction region amounts to solving a convex optimization problem. Also, approximation methods to build ellipsoidal prediction regions are provided. These approximations are useful when explicit descriptions of the regions are necessary. Finally, some numerical examples and comparisons for the case of a non-linear uncertain kite system are provided to prove the effectiveness of the proposed methodology.

Last-iterate convergence analysis of stochastic momentum methods for neural networks

The stochastic momentum method is a commonly used acceleration technique for solving large-scale stochastic optimization problems. Current convergence results of stochastic momentum methods under non-convex stochastic settings mostly discuss convergence in terms of the random output and minimum output, which requires temporal and spatial statistics of historical data. On the other hand, the last-iterate convergence allows us to avoid storing or selecting past output iterates after each iteration, while maintaining rigour in convergence analysis. To this end, we address the convergence of the last iterate output (called last-iterate convergence) of the stochastic momentum methods for non-convex stochastic optimization problems, in a way which is conformal with traditional optimization theory. For generality, we prove the last-iterate convergence of the stochastic momentum methods under a unified framework, covering both stochastic heavy ball momentum and stochastic Nesterov accelerated gradient momentum, whose momentum factors can be either constant or time-varying coefficients. Finally, the last-iterate convergence of the stochastic momentum methods is verified on the benchmark MNIST and CIFAR-10 datasets. The implementation of SUM is available at: https://github.com/xudp100/SUMhttps://github.com/xudp100/SUM.

A Bayesian Filtering Method for Wiener State-Space Systems Utilizing a Piece-wise Linear Approximation

In this paper, we develop a filtering algorithm for Wiener systems written in state-space form which considers correlated noise sources. The output non-linearity is approximated by using a piece-wise linear function. The probability function of the output signal conditioned to the system state is written as a Gaussian mixture distribution. A Gaussian sum filter algorithm to obtain the a posteriori probability density function of the state given the current and past output is developed. The associated statistics of the system state are obtained. The benefits of our proposal are illustrated via numerical simulations.

Kernel-Based Identification of Incrementally Input-to-State Stable Nonlinear Systems

Methods based on Reproducing Kernel Hilbert Spaces (RKHS) have proven to be a valuable tool for the identification of linear time-invariant systems in both discrete- and continuous-time. In particular, unlike most other techniques, they enable to systematically confer a priori desirable properties, such as stability, on the estimated models. However, existing RKHS methods mainly target impulse responses and, hence, do not extend well to the context of nonlinear systems. In this work, we propose a novel RKHS-based methodology for the identification of discrete-time nonlinear systems guaranteeing that the identified system is incrementally input-to-state stable (δISS). We model the identified system using a predictor function that, given past input and output samples, yields the output prediction at the next time instant. The predictor is selected from an RKHS by solving a constrained optimization problem that guarantees its δISS properties. The proposed approach is validated via numerical simulations.

Input Mapping Design for Batch-to-Batch Optimization With Limited Memory

This brief discusses data-driven design techniques for batch-to-batch optimization problems and proposes a new input-mapping-based online uncertainty compensation method for optimization-based iterative learning control (ILC) with limited memory. Since process uncertainties are generally inevitable, we collect historical data that incorporates past inputs and outputs to provide more insight into plant uncertain dynamics, resulting in a more accurate optimization model for optimal ILC solution. Instead of learning from a single step, our proposed method maintains a memory of the latest executed steps and updates the ILC solution using a linear combination of the memory and quadratic programming. A rigorous theoretical analysis shows that such a design provides robust benefits as well as asymptotic and monotonic stability properties under mild conditions. Finally, we demonstrate our design through an illustrative numerical example.