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Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data.

A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements.

The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions.

The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.

This research paper aims to optimize the calibration process for an ABB IRB 120 robot, specifically for robotic orbital milling applications, by introducing and validating the use of the observability index and telescopic ballbar for accuracy enhancement.

The study uses the telescopic ballbar and an observability index for the calibration of an ABB IRB 120 robot, focusing on robotic orbital milling. Comparative simulation analysis selects the O₃ index. Experimental tests, both static and dynamic, evaluate the proposed calibration approach within the robot’s workspace.

The proposed calibration approach significantly reduces circularity errors, particularly in robotic orbital milling, showcasing effectiveness in both static and dynamic modes at various tool center point speeds.

The study focuses on a specific robot model and application (robotic orbital milling), limiting generalizability. Further research could explore diverse robot models and applications.

The findings offer practical benefits by enhancing the accuracy of robotic systems, particularly in precision tasks like orbital milling, providing a valuable calibration method.

While primarily technological, improved robotic precision can have social implications, potentially influencing fields where robotic applications are crucial, such as manufacturing and automation.

This study’s distinctiveness lies in advancing the accuracy and precision of industrial robots during circular motions, specifically tailored for orbital milling applications. The innovative approach synergistically uses the observability index and telescopic ballbar to achieve these objectives.

This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

Traditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.

A novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.

The new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.

This study has positive implications for enriching the method system of multivariable grey prediction model.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

This study aimed to develop a psychometrically reliable job satisfaction (JS) measure for university employees, guiding administrative decisions and monitoring satisfaction over time in public universities.

A JS survey developed by a Brazilian federal university’s sustainability committee containing 58 items across physical, cognitive and organizational domains was longitudinally tested with 1,214 responses collected. The data were analyzed using Item Response Theory (IRT) analysis, employing the Graded Response Model, with tools such as frequency analysis, item characteristic curve, and full-information factor analysis in RStudio. The scale’s criterion validity was also established via expert qualitative interpretation.

The instrument’s internal consistency was confirmed as the results demonstrated its high reliability with a marginal reliability coefficient of 0.95. Significant findings revealed that recognition and supervisor relationships were key discriminators of JS and that workers began to perceive satisfaction when basic environmental conditions were met.

It is important to mention that the application of this scale is specifically limited to higher education institutions and may not be directly applicable to other educational settings or industry sectors without modifications.

Although numerous measures and scales have been developed to assess JS, one elaborated by using IRT in a public university environment was lacking. Due to shifting dynamics in the workplace, traditional measurement of JS has proven inadequate, necessitating a more precise, accessible and updated tool. The developed scale allows precisely targeted interventions to improve JS and can be reapplied to evaluate their effectiveness. This research thus contributes a valuable tool for academic organizational psychology, enhancing the understanding of the measurement of JS.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

While firms in developing countries are increasingly adopting lean practices of inventory management, there is limited evidence showing the impact of lean practices on firm performance in countries such as India. Lean practices improve the financial performance of the firms through superior cost-reduction measures and operational efficiencies. This paper examines the impact of inventory leanness in Indian manufacturing firms on their financial performance.

The authors measure inventory leanness based on stochastic frontier analysis (SLA), apart from using conventional measures available in the literature. The authors analyze the impact of inventory leanness on the financial performance of firms by examining data for 12,334 unique Indian manufacturing firms for the period 2009–2018. The authors present a comparative analysis using different methods of inventory leanness and study the effects on firm performance.

First, the authors find that only 68 industries out of 411 industries follow lean practices, i.e. most industries do not follow lean practices. Second, the estimation results show that there exists a positive relationship between inventory leanness and firm performance. The results suggest that an inverted U-shaped relationship exists between inventory leanness and firm performance for the entire sample. In particular, 17% of the industries in the sample exhibit such a relationship, and it is sufficiently strong to show up in the average regression results for the entire sample.

The authors introduce a novel measure of inventory leanness named stochastic frontier leanness based on the SFA method used in production economics. It measures leanness by benchmarking the inventory levels against the industry “frontier”. Furthermore, the authors conduct an empirical study of the lean-financial performance relationship with a large panel dataset of Indian firms instead of the survey-based methods that were previously used in the literature.

The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.