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This paper aims to study the effect of the COVID-19 economic slowdown on the restaurant industry in Jalisco, Mexico, identifying business-specific variables that improve/worsen restaurants’ odds of permanent closure.

The data of a randomized survey on 438 restaurants conducted in October 2020 in Jalisco, Mexico, are analyzed using a binary logistic regression model in which the dependent variable depicts the perception of the restaurant owner regarding the possibility of closing the business for good because of COVID-19.

Layoffs and large year-on-year drops in sales increased the odds of permanent closure by 12.7 and 5.5 times, respectively. At the same time, being a small business had a protective effect against closure. For instance, a restaurant with 6 to 10 employees and 11 to 20 seats, respectively, had 87.9% and 45.1% lower odds of permanent closure than a different-sized restaurant. There is also an element of legacy in restaurant resilience. Every year the business has been open, it has 2.5% lower odds of permanent closure.

These results call for government financial support to the restaurant industry in extreme financial distress and help to understand the business-specific characteristics of resilient restaurants when liquidity vanishes, such as in the COVID-19 economic crisis.

This study fills a gap in the literature regarding the effect of COVID-19 on the restaurant industry in Mexico, which is scarcely studied. Moreover, it analyzes data collected in the recovery period after the first wave of COVID-19, providing a unique scenario to study critical variables for the resilience of restaurants.

Este documento estudia el efecto de la desaceleración económica de COVID-19 en la industria de restaurantes en Jalisco, México, identificando variables específicas del negocio que mejoran/empeoran las probabilidades de cierre permanente de los restaurantes.

Los datos de una encuesta aleatoria sobre 438 restaurantes realizada en octubre de 2020 en Jalisco, México, se analizan utilizando un modelo de regresión logística binaria en el que la variable dependiente representa la percepción del propietario del restaurante con respecto a la posibilidad de cerrar el negocio para siempre debido a COVID-19.

Los despidos y las grandes caídas interanuales en las ventas aumentaron las posibilidades de cierre permanente en 12.7 y 5.5 veces, respectivamente. Al mismo tiempo, ser una pequeña empresa tenía un efecto protector contra el cierre. Por ejemplo, un restaurante con 6 a 10 empleados y de 11 a 20 asientos, respectivamente, tenía 87.9% y 45.1% menos posibilidades de cierre permanente que un restaurante de diferente tamaño. También hay un elemento de legado en la resiliencia de los restaurantes. Cada año que el negocio ha estado abierto, tiene un 2.5% menos de posibilidades de cierre permanente.

Estos resultados respaldan la necesidad de apoyo financiero del gobierno a la industria restaurantera en periodos de dificultades financieras extremas y ayudan a comprender las características específicas de los restaurantes resilientes cuando la liquidez desaparece, como en la crisis económica de COVID-19.

Este estudio llena un vacío en la literatura sobre el estudio del efecto del COVID-19 en la industria de restaurantes en México, que apenas se ha estudiado. Además, analiza datos recolectados en el período de recuperación después de la primera ola de COVID-19, proporcionando un escenario único para estudiar variables clave para la resiliencia de los restaurantes.

Este artigo estuda o efeito da desaceleração econômica COVID-19 na indústria de restaurantes em Jalisco, México, identificando variáveis específicas do negócio que melhoram/pioram as chances de fechamento permanente dos restaurantes.

Os dados de uma pesquisa randomizada com 438 restaurantes realizada em outubro de 2020 em Jalisco, no México, são analisados por meio de um modelo de regressão logística binária em que a variável dependente retrata a percepção do dono do restaurante sobre a possibilidade de fechar definitivamente o negócio por causa da COVID-19.

Demissões e grandes quedas ano a ano nas vendas aumentaram as chances de fechamento definitivo em 12,7 e 5,5 vezes, respectivamente. Ao mesmo tempo, ser uma pequena empresa teve um efeito protetor contra o fechamento. Por exemplo, um restaurante com 6 a 10 funcionários e 11 a 20 lugares, respectivamente, teve 87,9% e 45,1% menos chances de fechamento permanente do que um restaurante de tamanho diferente. Há também um elemento de legado na resiliência dos restaurantes. A cada ano que o negócio é aberto, tem chances 2,5% menores de fechamento definitivo.

Esses resultados pedem apoio financeiro do governo para o setor de restaurantes em extrema dificuldade financeira e ajudam a entender as características específicas do negócio de restaurantes resilientes quando a liquidez desaparece, como na crise econômica COVID-19.

Este estudo preenche uma lacuna na literatura sobre o estudo do efeito do COVID-19 na indústria de restaurantes no México, que é pouco estudado. Além disso, analisa dados no período de recuperação após a primeira onda de COVID-19, fornecendo um cenário único para estudar variáveis-chave para a resiliência dos restaurantes.

This study aims to investigate how gender diversity and remuneration of boards of directors’ influence earnings quality for Spanish-listed firms.

The sample includes 105 nonfinancial Spanish firms from 2013 to 2018, corresponding to an unbalanced panel of 491 firm-year observations. The primary empirical method uses a Tobit semiparametric estimator with firm- and industry-level fixed effects and an innovative set of measures for earnings quality developed by StarMine.

Results exhibit a positive correlation between increased gender diversity and a firm’s earnings quality, suggesting that a gender-balanced board of directors is associated with more transparent financial reporting and informative earnings. We also find a nonmonotonic, concave relationship between board remuneration and earnings quality. This indicates that beyond a certain point, excessive board compensation leads to more opportunistic manipulation of financial reporting with subsequent degradation of earnings quality.

This study only covers nonfinancial Spanish listed firms and is silent about how alternative board features’ influence earnings quality and their informativeness.

This study introduces measures of earnings quality developed by StarMine that have not been used in the empirical literature before as well as measures of board gender diversity applied to a suitable Tobit semiparametric estimator for fixed effects that improves the precision of results. In addition, while most of the literature focuses on Anglo-Saxon countries, this study discusses board gender diversity and board remuneration in the underexplored context of Spain. Moreover, the hand-collected data set comprising financial reports provides previously untested board features as well as a nonlinear relationship between remuneration and earnings quality that has not been thoroughly discussed before.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

This paper aims to investigate the problem of estimating the angle of attack (AoA) and relative velocity for vertical axis wind turbine (VAWT) blades from computational fluid dynamics data.

Two methods are implemented as function objects within the OpenFOAM framework for estimating the blade’s AoA and relative velocity. For the numerical analysis of the flow around and through the VAWT, 2 D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are carried out and validated against experimental data.

To gain a better understanding of the complex flow features encountered by VAWT blades, the determination of the AoA is crucial. Relying on the geometrically-derived AoA may lead to wrong conclusions about blade aerodynamics.

This study can lead to the development of more robust optimization techniques for enhancing the variable-pitch control mechanism of VAWT blades and improving low-order models based on the blade element momentum theory.

Assessment of the reliability of AoA and relative velocity estimation methods for VAWT’ blades at low-Reynolds numbers using URANS turbulence models in the context of dynamic stall and blade–vortex interactions.

This study examines the impact of university science parks’ (USPs) capabilities on revenue generation and introduces regional innovation as a moderating variable. This study aims to provide insights into enhancing revenue generation and fully leveraging the role of USPs in promoting revenue generation.

This study employs system generalized method of moments (GMM) estimation for 116 universities in China from 2008 to 2020, using hierarchical regression analysis to examine the relationships between variables.

The findings suggest that USPs play a beneficial role in fostering revenue generation. Specifically, the provision of incubation funding demonstrates a positive correlation, while USPs size exhibits an inverted U-shaped pattern, with a threshold at 3.037 and a mean value of 3.712, highlighting the prevalent issue of suboptimal personnel allocation in the majority of USPs. Moreover, the analysis underscores the critical moderating influence of regional innovation, affecting the intricate interplay between USPs size, incubation funding and revenue generation.

The single country (China) analysis relied solely on the use of secondary data. Future studies could expand the scope to include other countries and employ primary data collection. For instance, future research can further examine how regional development and USPs strategic plan impact revenue generation.

The study recommends that USPs managers and policymakers recognize the importance of incubation funding and determine the optimal quantity of USPs size to effectively foster revenue generation in USPs. Policymakers can use regional innovation as a moderating variable to reinforce the relationship between USPs size and incubation funding on revenue generation.

The study’s findings can contribute to the strategic industry growth and economic development of nations by promoting revenue generation. Leveraging the role of USPs and implementing the study’s recommendations can strengthen innovation and technology capabilities, driving strategic industry growth and economic development. This can enhance global competitiveness and promote sustainable economic growth.

This study introduces regional innovation as a moderating variable and provides empirical evidence of its influence on the relationship between USPs size and incubation funding on revenue generation. This adds value to research to the existing literature on USPs and revenue generation by showcasing the importance of examining the regional impact in research and innovation.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

The purpose of this study is to examine how informational faultlines on a board affect the management of knowledge owned by directors and the consequences on organizational performance. In this study, informational faultlines are defined as hypothetical lines that divide a group into relatively homogeneous subgroups based on the alignment of several informational attributes among board members.

The study uses unique hand-collected panel data covering 7,247 board members at 106 publicly traded firms to provide strong support for the hypothesized U-shaped relationship. The authors use a fixed effects approach and a system generalized method of moments approach to test the hypothesis.

The study finds that the relationship between informational faultlines on a board and organizational performance is U shaped, with the least optimal organizational performance experienced when boards have moderate informational faultlines. More specifically, informational faultlines within boards are negatively related to organizational performance across the weak-to-moderate range of informational faultlines and positively related to organizational performance across the moderate-to-strong range.

By explaining the mechanisms through which informational faultlines are related to organizational performance, the authors contribute to the literature in a number of ways. By conceptualizing how the management of knowledge plays an important role in the particular setting of corporate boards, the authors add not only to literature on knowledge management but also to the faultline and corporate governance literature.

This study offers a rationale for prior mixed findings by providing an alternative theoretical basis to explain the effect of informational faultlines within boards on organizational performance. To advance the field, the authors build on the concept of knowledge demonstrability to illuminate how informational faultlines affect the management of knowledge within boards, which will translate to organizational performance.

This research aims to present a novel cooperative control architecture designed specifically for roads with variations in height and curvature. The primary objective is to enhance the longitudinal and lateral tracking accuracy of the vehicle.

In addressing the challenges posed by time-varying road information and vehicle dynamics parameters, a combination of model predictive control (MPC) and active disturbance rejection control (ADRC) is employed in this study. A coupled controller based on the authors’ model was developed by utilizing the capabilities of MPC and ADRC. Emphasis is placed on the ramifications of road undulations and changes in curvature concerning control effectiveness. Recognizing these factors as disturbances, measures are taken to offset their influences within the system. Load transfer due to variations in road parameters has been considered and integrated into the design of the authors’ synergistic architecture.

The framework's efficacy is validated through hardware-in-the-loop simulation. Experimental results show that the integrated controller is more robust than conventional MPC and PID controllers. Consequently, the integrated controller improves the vehicle's driving stability and safety.

The proposed coupled control strategy notably enhances vehicle stability and reduces slip concerns. A tailored model is introduced integrating a control strategy based on MPC and ADRC which takes into account vertical and longitudinal force variations and allowing it to effectively cope with complex scenarios and multifaceted constraints problems.

Considering that the rate of profit constitutes a key indicator for the analysis of the evolution of capitalist economies, this chapter proposes to study the case of France from 1896 to 2019, that is, over 124 years in total. From a series of stock of productive capital reconstructed for the occasion, a rate of profit is calculated at the macroeconomic level within a conceptual framework faithful to Marx. Over this period of more than a century, three successive long waves are identified, as parts of a secular trend toward the fall in the French rate of profit. The latter, however, recovered several times during these three subperiods, but finally reoriented downwards, with fluctuations of an amplitude tending to decrease more and more and a deployment in a decreasing spiral of French capitalism. This long-term downward trend is mainly due to the rise in the organic composition of capital.