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The displaced commercial risk is one of the specific risks in the Islamic finance that creates a serious debate among practitioners and researchers about its management. The purpose of this paper is to assess a new approach to manage this risk using machine learning algorithms.

To attempt this purpose, the authors use several machine learning algorithms applied to a set of financial data related to banks from different regions and consider the deposit variation intensity as an indicator.

Results show acceptable prediction accuracy. The model could be used to optimize the prudential reserves for banks and the incomes distributed to depositors.

However, the model uses several variables as proxies since data are not available for some specific indicators, such as the profit equalization reserves and the investment risk reserves.

Previous studies have analyzed the origin and impact of DCR. To the best of authors’ knowledge, none of them has provided an ex ante management tool for this risk. Furthermore, the authors suggest the use of a new approach based on machine learning algorithms.

This paper aims to study the possibility of electroplating copper coatings on chemically and chemical-galvanically nickel-plated acrylic fibers, to be further processed into yarn, fabrics, knitwear and nonwoven materials.

Electrically conductive fibers with different copper contents have been obtained, and the effect of electrolyte pH, its composition, current strength at the first and second cathodes, as well as the metallization time on the electrophysical, physical and mechanical properties of copper-containing fibers, has been studied.

The studies have shown that with an increase in the copper content, the electrical conductivity, the uniformity of the coating and the uniformity of the electrophysical properties (for chemical-galvanically nickel-plated fiber) increase. In the case of copper plating of chemically nickel-plated fiber, the coefficient of variation in electrical resistance increases with increasing plating time, even though the copper content increases, and the coefficient of variation in copper content and electrical resistance decreases. The physical and mechanical properties of copper-containing fibers differ slightly from the original (subjected to copper plating) and industrial Nitron fibers. With copper plating, the strength of the fiber practically does not decrease, and the elongation decreases somewhat, compared with the mass-produced Nitron fiber.

The physical and mechanical properties of copper-containing fibers are quite high, which makes it possible to be successfully further processed into yarn, fabrics, knitwear and nonwoven materials.

Niobium Nitride (NbN) was interesting material for its applications in the medicinal tools or tools field (corresponding to saline serum media) as well as in mechanical properties. The aim of this work was depositing NbN thin films on two types of substrates (stainless steel (SS304) and silicon (100)) using plasma technique at varied powers (100–150 W).

DC magnetron sputtering technique at different powers were used to synthesis NbN films. Film structure was studied using X-ray diffraction (XRD) pattern. Rutherford elastic backscattering and energy dispersive X-ray were used to examine the deposited film composition. The films morphology was studied via atomic force microscopy and scanning electron microscopy images. Corrosion resistance of the three NbN/SS304 films was studied in 0.9% NaCl environment (physiological standard saline).

All properties could be controlled by the modification of DC power, where the crystallinity of samples was changed and consequently the corrosion and microhardness were modified, which correlated with the power. NbN film deposited at higher power (150 W) has shown better corrosion resistance (0.9% NaCl), which had smaller grain size (smoother) and was thicker.

The NbN films have a preferred orientation (111) matching to cubic structure phase. Corrosion resistance was enhanced for the NbN films compared to SS304 substrates (noncoating). Therefore, NbN films deposited on SS304 substrate could be applied as medicinal tools as well as in mechanical fields.

Systemic risk is of concern for economic welfare as it can lower the credit supply to all the sectors within an economy. This study examines for the first time the complete hierarchy of variables that drive systemic risk during normal and crisis periods in Pakistan, a developing economy.

Secondary data of the bank, sector and country variables are used for the purpose of the analysis spanning from 2000 to 2020. Systemic risk is computed using marginal expected shortfall (MES). One-step and two-step system GMM is performed to estimate the impact of firm, sector and country-level variables on systemic risk.

The findings of the study highlight that sector-level variables are also highly significant in explaining the systemic risk dynamics along with bank and country-level variables. In addition, economic sensitivity influences the significance level of variables across crisis and post-crisis periods and modifies the direction of relationships in some instances.

The study examines the systemic risk of a developing economy, and findings may not be generalizable to developed economies.

The outcome of the study provides a comprehensive framework for the central bank and other regulatory authorities that can be translated into timely policies to avoid systemic financial crisis.

The negative externalities generated by systemic risk also affect the general public. The study results can be used to avoid the systemic financial crisis and resultantly save the loss of the general public's hard-earned holdings.

The firm, sector and country-level variables are modeled for the first time to estimate systemic risk across different economic conditions in a developing economy, Pakistan. The study can also act as a reference for researchers in developed economies as well regarding the role of sector-level variables in explaining systemic risk.

The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique.

DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO₂ thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films.

Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO₂ (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness.

DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

Commercial banks face several risks, including credit, liquidity, operational and disruptive risks. In addition to these risks that are challenging for banks to control and manage, crises and disasters can exert substantially more destructive shocks. These shocks can exacerbate internal risks and cause severe damage to the bank's performance, leading banks to bankruptcy and closure. This study aims to facilitate achieving resilient banking policies through a model-based assessment of business continuity management (BCM) policies.

By applying a system dynamics (SD) methodology, a systemic model that includes a causal structure of the banking business is presented. To build a simulation model, data are collected from a commercial bank in Iran. By presenting the simulation model of the bank's business, the consequences of some given crises on the bank's performance are tested, and the effectiveness of risk and crisis management policies is evaluated. Vensim Personal Learning Edition (PLE) software is used to construct the simulation model.

Results indicate that the current BCM policies do not show appropriate resilience in the face of various crises. Commercial banks cannot create sustainable value for the banks' shareholders despite the possibility of profitability, as the shareholders lack adequate resilience and soundness. These commercial banks do not have the appropriate resilience for the next pandemic after coronavirus disease 2019 (COVID-19). Moreover, the robustness of the current banking business model is very fragile for the banking run crisis.

A forward-looking view of resilient banking can be obtained by combining liquidity coverage, stable funding, capital adequacy and insights from stress tests. Resilient banking requires a balanced combination of robustness, soundness and profitability.

The present study is a combination of bank business management, risk and resilience management and SD simulation. This approach can analyze and simulate the dynamics of bank resilience. Additionally, present of a decision support system (DSS) to analyze and simulate the outcomes of different crisis management policies and solutions is an innovative approach to developing effective and resilient banking policies.

This paper aims to examine the impact of COVID-19 on inflation in Indonesia. There are two questions in this study: (1) Is there an impact of COVID-19 on inflation in Indonesia? and (2) whether there are differences in the impact of COVID-19 on regional inflation in Indonesia, considering the different intensities associated with COVID-19.

The estimation technique showing the impact of the COVID-19 pandemic on inflation uses the difference-in-differences (DID) method described by Pischke (2008). The core idea of the estimation above is continuous DID using panel data. No province was affected by COVID-19 before 2020:Q1. Once COVID-19 hits the economy, the effects vary from one district to the other.

The authors find that the severity of the COVID-19 pandemic negatively affects inflation – the more severe the pandemic, the lower the inflation. This finding conforms with several studies suggesting higher demand pressures than supply during the pandemic. Compared with supply-side indicators such as production index, demand-side indicators – such as consumer confidence index and real sales index – fell more sharply.

In the Introduction section, the authors have added a discussion that indeed the COVID-19 pandemic affects inflation through both the demand- and supply-side shocks. While factors driving regional differences in inflation rate are important research and policy questions, the analysis of these factors is outside the scope of this study. The study focuses on the COVID-19 impact on inflation and whether the pandemic disproportionately affects some regions than the others.

This research is important to provide an understanding of the nature of the pandemic on inflation in the context of the Indonesian economy, which is essential to policy formulation, especially for the Central Bank in carrying out the mandate to maintain rupiah stability. This issue is due to the implications of different policy responses between demand- and supply-side shocks.

As a novelty in this study and research gap, the authors use a continuous DID method to account for the varying intensity of COVID-19 across the provinces. In particular, the authors use the number of positive cases of COVID-19 per 1,000 population as opposed to just a binary indicator of before-and-during COVID-19 across provinces.

The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).

The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.

The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.

The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

In this study, the authors argue that the private information obtained and transmitted by institutions during the corporate visits can alleviate the degree of information asymmetry between firms and investors, so institutional visits may influence investors' heterogeneous beliefs. Therefore, the authors investigated whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs.

This study examines whether and how institutional investors' corporate visits affect investors' heterogeneous beliefs using the data of A-share companies from the Shenzhen Stock Exchange (SZSE) during 2013–2019. Using empirical research method, this study designs and conducts an empirical research according to empirical research's basic norms.

The authors find that institutional visits effectively decrease investors' heterogeneous beliefs, especially institutional investors. Meanwhile, institutional site visits and sell-side institutional visits have a more significant negative effect on investors' heterogeneous beliefs. The findings remain after robustness tests with the alternative variable, instrumental variable, propensity score matching and quantile regression methods.

The development of China's capital market is imperfect, resulting in a strong speculative atmosphere. So, investors' irrational investment behaviors occur from time to time, leading to sizeable heterogeneous beliefs in China's capital market, which increases the risk of investment and is not conducive to the discovery of corporate value and the efficient allocation of resources. Therefore, exploring the factors influencing heterogeneous beliefs and finding ways to alleviate heterogeneous beliefs can reduce the proportion of speculative investors and promote the healthy development of China's capital market.