We shed light on the narrative that listing contributes to risk-taking by examining the risk characteristics of listed BHCs, small enough to be private, against a sample of comparable private BHCs, large enough to be listed, over the 1987-2019 period. We measure our proxies for risk characteristics over different intervals in the sample period to account for the effect of new regulations and variation in the intensity of information production by regulators, markets, and financial firms. We document that listed banks are riskier than private banks over the 22-year sample period. Examining the subperiods, we find that listed banks are riskier than private banks before the crisis, but they may not be as risky following the crisis. While risk increases for all banks during the crisis, the increase in risk for listed banks during the crisis is greater than that for private banks. Our findings are both statistically and economically significant and suggest that financial reforms and regulatory expectations facing banks post-crisis might have contributed to the risk reduction for listed banks relative to private banks.

ABSTRACTThis paper examines the impact of bank capital on the capital structure of nonfinancial firms, focusing on lenders and commercial borrowers from 2000 to 2019. We find a positive relationship between firm leverage and bank capital, with lending serving as a key channel for this effect. Additionally, increased lending is associated with higher firm risk and slower growth. Our deal‐level analysis reveals consistent findings with those at the firm level: greater lending is linked to higher spreads, more tranches, more secured loans, fewer lenders per deal, and longer maturities, all of which indicate increased borrower risk. This study offers new insights into how bank capital structure policies influence the financial structure of nonfinancial firms and contributes to the broader debate on the spillover effects of risk‐reduction measures in the financial sector, such as capital regulation, on the real economy.

This paper presents a computational procedure for the determination of the dynamic behaviour of graphene with different types of defects. The lattice of graphene is modelled using the molecular structural mechanics (MSM) approach, where the C-C covalent bonds are replaced by equivalent beam elements. Cantilever and bridged boundary conditions are applied for the analysis. Four types of Stone-Wales (SW) defects such as S-W (555-8), S-W (555-888-3), S-W (555-77-8) and S-W (888-3), and two types of pinhole defects with 6 and 24 elements eliminated are examined on armchair, zigzag and chiral type of graphene sheet. The effect of the structural length of the sheet, chirality and defect type on the vibrational properties of graphene sheets is investigated. The computed results reveal that SW defects produce a high frequency to that of pristine graphene, whereas the effect of pinhole defects is significant as compared to SW defects. The computed results will be useful in nano-resonator-based sensor applications.

Iridoviruses, a group of double-stranded DNA viruses, pose a significant threat to various aquatic animals, causing substantial economic losses in aquaculture and impacting ecosystem health. Early and accurate detection of these viruses is crucial for effective disease management and control. Conventional diagnostic methods, including polymerase chain reaction (PCR) and virus isolation, often require specialized laboratories, skilled personnel, and considerable time. This highlights the need for rapid, sensitive, and cost-effective diagnostic tools for iridovirus detection. Single-layer graphene, a two-dimensional material with unique properties like high surface area, excellent electrical conductivity, and chemical stability, has emerged as a versatile platform for biosensing applications. This paper explores the potential of employing single-layer graphene in the development of a bionanosensor for the sensitive and rapid detection of iridoviruses. The aim of the present investigation is to develop a sensor by analyzing the vibrational responses of single-layer graphene sheets (SLGS) with attached microorganisms. Graphene-based virus sensors typically rely on the interaction between the virus and the graphene surface, which lead to changes in the frequency response of graphene. This change is measured and used to detect the presence of the virus. Its high surface-to-volume ratio and sensitivity to changes in its frequency make it a highly sensitive platform for virus detection. We employ finite element method (FEM) analysis to model the sensor’s performance and optimize its design parameters. The simulation results highlight the sensor’s potential for achieving high sensitivity and rapid detection of iridovirus. Bridged and simply supported with roller support boundary conditions applied at the ends of SLG structure. Simulations have been performed to see how SLG behaves when used as sensors. A single-layer graphene armchair SLG (5,5) with 50-nm length exhibits its highest frequency vibration at 8.66 × 106 Hz, with a mass of 1.2786 Zg. In contrast, a zigzag-SLG with a (18,0) configuration has its lowest frequency vibration at 2.82 × 105 Hz. This aids in comprehending the thresholds of detection and the influence of factors such as size, and boundary conditions on sensor effectiveness. These biosensors can be especially helpful in biological sciences and the medical field since they can considerably improve the treatment of patients, cancer early diagnosis, and pathogen identification when used in clinical environments.

Culture deeply infuses entrepreneurs' psychology, and ultimately the performance of their firms. Drawing on cultural-tightness looseness theory and emergent entrepreneurship research on intercultural cognition, we introduce the concept of cultural tightness emancipation. We examine how culturally tight home contexts can be especially psychologically restrictive for underdog entrepreneurs, with what we term ‘layers of tightness’, such as gender role and family expectations, compounding these dynamics. Drawing on multi-wave data in Nicaragua, we theorize that culturally loosening experiences can aid entrepreneurs in gaining broader perspectives and skills that bolster the profitability of their ventures. Specifically, we probe how time spent living overseas can yield enduring positive effects on founders years later when running their businesses back home. In line with our theorizing, we find support that female entrepreneurs running family businesses in culturally tight Nicaragua benefit considerably more from such cultural tightness emancipation. Implications for research and practice are discussed.

In this study the authors investigated the vibrations performance of chiral multi-walled carbon nanotubes (MWCNTs). They examined chiral MWCNTs with attached bacteria positioned at both the tip and middle sections of the nanotubes. The main goal of this research was to create a sensor with the ability to detect and distinguish bacteria or viruses that could potentially adhere to the surfaces of chiral MWCNTs. The authors considered two boundary conditions, fixed-fixed and fixed -free for the chiral MWCNT. They utilized a molecular structural mechanics approach. In this approach the vibrational responses of chiral MWCNT-based nano-biosensors with different diameters of the chiral MWCNTs were examined. The study's objective was to fill research gaps by introducing an innovative approach for detecting defects in chiral MWCNTs through vibrational analysis. They simulated 432 MWCNT samples to investigate the vibrational performance of defective chiral MWCNTs. The results suggested that the first mode of the vibrational frequency of the chiral MWCNT is particularly significant for sensing applications. It was observed that as the percentage of vacancy defects increased, the natural frequency decreased for both boundary conditions.

Through experimental observations and reports, various challenges have been identified in carbon nanotubes (CNT), including Stone Wales (SW) flaws and position flaws. Among these imperfections, point vacancies are the most prevalent in the CNT lattice. However, there is currently no established method for detecting these issues, and the influence of these flaws on the vibrational properties of three-walled carbon nanotubes (TWCNTs) remains uncertain. This research paper introduces a novel approach that utilizes vibrational analysis to detect flaws in TWCNTs. By conducting the first investigation into the impact of point vacancies on the vibrational modal frequencies of TWCNTs, our study bridges these knowledge gaps. This study examines the impact of defect quantity on various types of TWCNTs and investigates the vibrational properties of TWCNTs with point vacancies using a molecular structural mechanics technique. A total of 432 TWCNT models were simulated using molecular structural mechanics (MSM), and their modes were identified through finite element (FE) analysis. The fundamental vibration's natural frequency in TWCNTs with defects was then determined. The findings indicate that the depth of the mode shape is influenced by the TWCNTs' diameter, the extent of point vacancy defects, and the boundary condition. It was observed that as the number of vacancy defects increases from 0 to 4 10^-21 gm a given attached mass, which follows the sequence of chiral, armchair, and zigzag TWCNTs.

Context Nanosensors and actuators are frequently made of graphene. Any defect in the graphene’s manufacturing has an impact on its sensing performance and on its dynamic behaviour. Using a molecular dynamics technique, the influence of pinhole defects and atomic defects on the performance parameters of single-layer graphene sheets (SLGSs) and double-layer graphene sheets (DLGSs) with various boundary conditions and lengths is explored. In contrast to the perfect nanostructure of a graphene sheet, defects are described as holes formed by atomic vacancies. As the number of defects increases, the simulation results show that the presence of defects has the greatest impact on the resonance frequency of SLGSs and DLGSs. The influence of pinhole defect (PD) and atomic vacancy defect (AVD) on armchair, zigzag, and chiral SLGSs and DLGSs was investigated in this article using molecular dynamics simulation. The influence of both types of defects is largest when it is adjacent to the fixed support for all three different types of graphene sheets, i.e. armchair, zigzag, and chiral. Methods The structure of the graphene sheet has been created using ANSYS APDL software. In the structure of the graphene sheet, atomic and pinhole defects have been generated. SLG and DLG sheets are modelled using a space frame structure that is identical to a three-dimensional beam. Dynamic analysis of single-layer and double-layer graphene sheets performed with different lengths using the atomistic finite element method. The interlayer separation in the form of Van der Waals interaction is modelled using characteristic spring element (Combin14). The upper and lower sheets of DLGSs are described as elastic beams connected by a spring element. With atomic vacancy defect for the bridged boundary condition, the highest frequency of 2.86 × 10 8 Hz was found for zigzag DLG (20 0) and with same boundary condition for pinhole defect 2.79 × 10 8 Hz frequency achieved. In a single-layer graphene sheet with an atomic vacancy and cantilever boundary condition, the maximum efficiency was 4.13 × 10 3 Hz for SLG (20 0), while in a pinhole defect, it produced 2.73 × 10 7 Hz. Moreover, the elastic parameters of beam components are calculated using the mechanical properties of covalent bonds between carbon atoms in the hexagonal lattice. The model has been tested against previous research. The focus of this research is to develop a mechanism for determining how defects affect graphene frequency band in application as nano resonators.

CONTEXT:Graphene based nano sensors have huge potential in an era of sensor technology. The objective of this study is to create a sensor by investigating the vibration responses of cantilever and bridged boundary conditioned single layer graphene sheets (SLGS) with various attached microorganisms on the tip and at the centre of the sheet. The Parvoviridae, Flaviviridae, and Polyomaviridae biological substances have been comprehensively investigated here. For the Parvoviridae, Polyomaviridae, and Flaviviridae categories of targeted microbes, the sizes are 21nm, 40nm, and 45nm, respectively. The Parvoviridae family has a maximum frequency of 1.87x107 Hz with a cantilever condition and a mass of 4.2441 Zg, and for a bridged condition, it demonstrates a maximum frequency of 1.23x108 Hz with the same mass on armchair SLG (5 5). The data analysis shows that 3.0041 Zg mass of the Mimivirus has the lowest frequency. It demonstrates explicitly that the rate of frequency decreases as the value of mass increases. When compared to chiral SLG, the armchair single layer graphene sheet performs better. The research indicates that the dynamic properties are significantly influenced by the mass of various biological organisms. The application of this sensor will enable the detection of microorganisms or viruses that can be connected to SLG.METHODS:In this research, the application of Single Layer Graphene (SLG) as a virus sensing device is explored. Atomistic finite element method (AFEM) has been used to carry out the dynamic analysis of SLG. Molecular dynamic analysis and simulations have been performed to see how SLG behaves when employed as sensors for biological entities and when they are exposed to bridged and cantilever boundary conditions. The frequency analysis was performed using ANSYS APDL software. SLG of various chirality has been utilised in the investigation. By altering the applied mass of a biological object, the difference in frequency observed. The idea behind mass detection employing nano biosensors is built on the concept that the stiffness of a biomolecule changes as its mass changes, making the resonant frequency extremely sensitive to that change. A shift in the resonance frequency results from a change in the associated mass on the graphene sheet. The main challenge in mass detection is estimating the variation in resonant frequency driven by the mass of the connected molecule. The SLG-based biosensor has a specific application in the early identification of diseases. The biosensor investigated in this article is novel, whereas the biosensors that are presently on the market operate using the ionization method. The simulations result shows SLG based biosensor's sensitivity considerably faster than an existing one.

The presence of point vacancies and pin holes are the most common flaws found in the lattice structure of carbon nanotubes (CNTs). Additionally, impact of mention defects on the vibrational properties of three-walled carbon nanotubes (TWCNTs) remains unknown. Aim of study, to address these gaps by introducing a novel method for identifying defects in TWCNTs through vibrational analysis. The research employed a molecular structural mechanics technique to simulate 648 samples of TWCNTs, followed by finite element studies to determine their modes of vibration. The natural frequency of the basic vibration of flawed TWCNTs was then determined. It was observed that the natural frequency is inversely proportional to (i) the number of point vacancies and (ii) the diameter of the TWCNTs. Furthermore, the natural frequency exhibited an exponential increase after reaching a mass of 10−20gm in armchair and zigzag TWCNTs, while in chiral TWCNTs, the exponential increase occurred after reaching a mass of10−19gm. Notably, in TWCNTs with 0% and 0.5% vacancy defects, the natural frequency results were very close for chiral TWCNTs when compared to zigzag and armchair TWCNTs.

The impact of mass on the resonance frequency of single-layered pristine graphene is examined in this research paper. Graphene is viewed as a fascinating material in modern era. In mass sensing, graphene has a significant potential. The authors of this work explored the appliance of single layer graphene (SLG) as a sensing device. The dynamic analysis of SLG is carried out with various boundary conditions. The atomistic finite element technique is used to simulate SLG. SLG sheets have been modelled. The mechanical properties of covalently bound carbon atoms in a hexagonal structure are also used to compute the physical properties of beam elements. At nodes that correspond with carbon atoms, each beam element's mass is represented as a point mass. Simulations were done to see how SLG responded to different conditions of boundary and when used as a mass sensor. Changing the length and applied mass of SLGs reveals the frequency variation. The obtained results show that the length of the sheet and varied mass values have a substantial influence on the dynamic properties. The results reveal that when mass increase, the sensitivity of the SLGS-based mass sensor increases.

In modern manufacturing industries, automated machining systems have become a necessity. However, optimizing resource utilization and achieving a good surface finish remain challenging tasks. Excessive tool usage and poor surface finish are common problems encountered in turning centers, which affect productivity and product quality. In this research, we propose an approach that leverages automation and machine learning techniques to maximize tool use and improve surface finish. Our objective is to investigate the relationship between tool life and surface roughness and to develop a method that can optimize cutting parameters for turning centers. We have conducted an experimental study to evaluate the proposed approach, which involves the automatic determination of cutting parameters based on machine learning algorithms, and concluded a cutting speed of 43.10[Formula: see text]m/min, the surface finish achieved for aluminum material was 1.98[Formula: see text][Formula: see text]m. In the case of mild steel material, the surface finish was 12[Formula: see text][Formula: see text]m at a cutting speed of 25.13[Formula: see text]m/min. Similarly, for cast iron material, the surface finish was 8.45[Formula: see text][Formula: see text]m at a cutting speed of 30.16[Formula: see text]m/min. Our results show that the proposed method outperforms the traditional manual method in terms of surface finish, tool usage, and machining time. Our approach can be applied to other machining systems, providing a practical and effective solution to improve the efficiency and quality of machining processes. This paper presents an experiment that explores the relationship between tool life and surface roughness. Furthermore, an automated approach is proposed for eliminating G code in machining, which can improve the efficiency of machine tools and result in a better surface finish. Objective: To maximize tool use and improve surface finish in turning centers by incorporating automation and machine learning. Idea: This research aims to explore the use of automation and machine learning in turning centers to optimize the cutting parameters and achieve a better surface finish. Description of the idea: The study was conducted by performing experiments on three different materials, i.e., aluminum, mild steel, and cast iron. The cutting parameters, including spindle speed, feed, and depth of cut, were controlled by a programmable logic controller (PLC) integrated with a tachometer and Vernier scale. The surface finish was measured using a surface roughness tester, and the data was analyzed using a supervised machine learning algorithm.

The study aims to examine the short run and long run relationship between bank nifty and other selected sectoral indices of NSE. 10 sectoral indices e.g. Current research has taken into account Nifty Bank, Nifty Auto, Nifty IT, Nifty FMCG, Nifty Media, Nifty Metal, Nifty Pharma, Nifty PSU, Nifty Private Banks, and Nifty Reality. We used the Unit Root lest, VAR, Descriptive Analysis, Johansen's Cointegration lest, Beta, and VECM to test our hypothesis. According to the study, there is no long-term association between the variables and the data was not steady at the level.Only short term relationship was seen between NiFTYINDEX and RETNIFTY_MEDIA. Beta justified that Nifty Pharma is most defensive sector i.e. it is least sensitive to changes occurring to Nifty, whereas Nifty Private bank is most sensitive sector. It was also seen that 3 sectors, Nifty_ Media, Nifty_PSU, and Nifty Phanna Indexes have negative correlation with Nifty index.

Abstract: Because of major developments in fundamental research and industrial applications, graphene's mass and low-cost production have become a vital step toward its real-world uses. Graphene, a one-atom-thick carbon crystal with a unique set of physical and chemical properties comprising extreme mechanical behaviour with excellent electrical and thermal conductivity, is emerging as a serious contender to replace many traditional materials in a variety of applications. Graphene has the potential to improve the performance, functionality, and durability of a broad spectrum of applications, but its commercialization will require more study. Applications and emerging techniques for the production of graphene have been investigated in this study. To increase the use of graphene, its current limitations must be solved expeditiously to improve its performance. In terms of applications, graphene's advantages have expanded its use in both electroanalytical and electrochemical sensors. This review paper highlights the most important experimental successes in graphene material manufacturing, as well as its changing characteristics in connection to smart applications. We explore how graphene may be successfully integrated directly into devices, enabling a wide range of applications such as transparent electrodes, photovoltaics, thermoelectricity, 3D printing, and applications in biomedical and bioimaging devices. Graphene's prospects are also explored and discussed.

: MWCNTs are elongated cylindrical nanoobjects made of sp2 carbon. They have a diameter of 3–30 nm and can grow to be several centimetres long. Therefore, their aspect ratio can range between 10 to 10 million. Carbon nanotubes are the foundation of nanotechnology. It is an exceptionally fascinating material. CNTs possess excellent properties, such as mechanical, electrical, thermal, high adsorption, outstanding stiffness, high strength and low density with a high aspect ratio. These properties can be useful in the fabrication of revolutionary smart nanomaterials. The demand for lighter and more robust nanomaterials in different applications of nanotechnology is increasing every day. Various synthesis techniques for the fabrication of MWCNTs, such as CVD, arc discharge, flame synthesis, laser ablation, and spray pyrolysis, are discussed in this review article, as are their recent applications in a variety of significant fields. The first section presents a brief introduction of CNTs, and then the descriptions of synthesis methods and various applications of MWCNTs in the fields of energy storage and conversion, biomedical, water treatment, drug delivery, biosensors, bucky papers and resonance-based biosensors are provided in the second section. Due to their improved electrical, mechanical, and thermal properties, MWCNTs have been extensively used in the manufacturing and deployment of flexible sensors.

Graphene has been widely and extensively used in mass sensing applications. The present study focused on exploring the use of single-layer graphene (SLG) and double-layer graphene (DLG) as sensing devices. The dynamic analysis of SLG and DLG with different boundary conditions (BDs) and length was executed using the atomistic finite element method (AFEM). SLG and DLG sheets were modelled and considered as a space–frame structure similar to a 3D beam. Spring elements (Combin14) were used to identify the interlayer interactions between two graphene layers in the DLG sheet due to the van der Waals forces. Simulations were carried out to visualize the behavior of the SLG and DLG subjected to different BDs and when used as mass sensing devices. The variation in frequency was noted by changing the length and applied mass of the SLGs and DLGs. The quantity of the frequency was found to be highest in the armchair SLG (6, 6) for a 50 nm sheet length and lowest in the chiral SLG (16, 4) for a 20 nm sheet length in the bridged condition. When the mass was 0.1 Zg, the frequency for the zigzag SLG (20, 0) was higher in both cases. The results show that the length of the sheet and the various mass values have a significant impact on the dynamic properties. The present research will contribute to the ultra-high frequency nano-resonance applications.

The authors use the FE research to analyse and predict the oscillational performance of TWCNTs (Triple-Walled Carbone Nanotubes) in this work. Different types of nanotubes like chiral, zigzag, and armchair as well as different lengths and diameters are used for analysis purpose. The novelty is authors included the chiral structure which is difficult to model for simulation purpose. The approach employed here to account for the variation in length in atomic models produced by removing hexagonal patterns has either never been utilised or has very little evidence. As a result, this technique's use is distinct and novel. Moreover, the authors investigate the effect of gradually removing the hexagonal patterns which contain the Carbon-Carbon(CC) bond ring in triple-walled carbon nanotube models. Molecular structural mechanics methodology is used for developing and simulations of armchair, zigzag and chiral triple walled carbon nanotube (TWCNTs). The process of removing the Carbon-Carbon bond ring from the outside wall of TWCNTs by maintaining the inside walled unchanged. Authors analysed the resonant frequencies for different attached masses ranging from 10−16 to 10−23 gm on TWCNTs and different boundary conditions for TWCNTs.

Abstract Nano sensors and actuators are frequently made of graphene. Any defect in the graphene's manufacturing has an impact on its sensing performance and on its dynamic behaviour. Using a molecular structure mechanics technique, the influence of pinhole defects and atomic defects on the performance parameters of single layer graphene sheets (SLGS) and double layer graphene sheets (DLGS) with various boundary conditions and lengths is explored. In contrast to the perfect nanostructure of a graphene sheet, defects are described as holes formed by atomic vacancies. As the number of defects increases, the simulation results show that the presence of defects has the greatest impact on the resonance frequency of SLG and DLG. The interlayer separation in the form of Van der Waals interaction is modelled using characteristic spring element. The upper and lower sheets of DLGS are described as elastic beams connected by a spring element. For the purposes of analysis, two distinct types of boundary conditions (BD) are identified: cantilever and bridged. The influence of pinhole defect (PD) and atomic vacancy defect (AVD) on armchair, zigzag, and chiral SLGS and DLGS was investigated in this article using FEM based atomic Molecular Structure. The influence of both types of defects is largest when it is adjacent to the fixed support for all three different types of graphene sheets, i.e., Armchair, Zigzag, and Chiral. The model has been tested against previous research. The focus of this research is to develop a mechanism for determining how defects affect graphene frequency band.

Bank capital requirements reduce the probability of bank failure and help mitigate taxpayers' sharing in the losses that result from bank failures. Under Basel III, direct capital requirements are supplemented with liquidity requirements. Our results suggest that liquidity provisions of banks are connected to bank capital and that changes in liquidity indirectly affect the capital structure of financial institutions. Liquidity appears to be another instrument for adjusting bank capital structure beyond just capital requirements. Consistent with Diamond and Rajan (2005), we find that liquidity and capital should be considered jointly for promoting financial stability.