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This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

First is to develop the time truncated median control chart for the Rayleigh distribution (RD) and generalized RD (GRD), respectively. Second is to evaluate the performance of the proposed attribute control chart which depends on the average run length (ARL) and third is to include real life examples for application purpose of the proposed attribute control chart.

(1) Select a random sample of size n from each subgroup from the production process and put them on a test for specified time t, where t = ? × µe. Then, count the numbers of failed items in each subgroup up to time t. (2) Step 2: Using np chart, define D = np, the number of failures, which also a random variable follows the Binomial distribution. It is better to use D = np chart rather than p chart because the authors are using number of failure rather than proportion of failure p. When the process is in control, then the parameters of the binomial distribution are n and p0, respectively. (3) Step 3: The process is said to be in control if LCL = D = UCL; otherwise, the process is said to be out of control. Hence, LCL and UCL for the proposed control chart.

From the findings, it is concluded that the GRD has smaller ARL values than the RD for specified values of parameters, which indicate that GRD performing well for out of control signal as compared to the RD.

This developed control chart is applicable when real life situation coincide with RD and GRD.

Researcher can directly use presented study and save consumers from accepting bad lot and also encourage producers to make good quality products so that society can take benefit from their products.

This article dealt with time truncated attribute median control chart for non-normal distributions, namely, the RD and GRD, respectively. The structure of the proposed control chart is developed based on median lifetime of the RD and GRD, respectively.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

The diffusion of technologies from other sectors, and innovations in kitchen equipment, fueled structural changes within the foodservice industry. However, this change comes at a price of disrupting the critical step of assessing the demand forecast accuracy. This study aims to explore a surprisingly unique and elevated complexity when assessing the critically important demand forecast accuracy.

The paper develops a mathematical model to describe and explore the nature of the problem in structural biased demand forecast accuracy assessment. It then uses numerical simulation to construct a market example to gain better insights on the bias characteristics. Finally, the forecast accuracy measurement’s inherent bias is contrasted with that of other typical hospitality forecasting setups.

This paper outlines the theoretical underpinnings of how demand forecasts in the central kitchen setup are dynamic and thus produce a structural bias. More specifically, this paper discovers how, in this context of orders from a central location, the forecasts set the capacity constraints, and, consequently, generate a considerably more biased forecast accuracy measure. Relying on such forecast accuracy measures can lead to serious negative business outcomes.

To the best of the author’s knowledge, this study is the first to show that in the unique new technology enabled environment of central kitchen operation, where daily dish demand forecasts set the daily constrained capacity levels, the accuracy measure is severely biased, and consequently accuracy is likely to deteriorate, which in turn, could lead to suboptimal decisions. The major theoretical contribution of this study is a novel analytical model which explains and describes the bias in the accuracy measurement.

技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。

本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。

本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。

这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

This paper develops a debt-run model to study the effects of liquidity injections on debt markets in the presence of a renegotiation option. In the model, creditors decide when to withdraw their funding and equityholders can renegotiate the contract terms of debt. We show that when equityholders have a large bargaining power, liquidity injections into distressed firms can rather cause more aggressive runs from their creditors, hurting the debt value. This outcome occurs because equityholders can strategically utilize the renegotiation option as a bankruptcy threat, pushing down the debt value below the potential liquidation value of the firm. In such a scenario, a deterred default resulting from emergency capital injections could be detrimental to creditors.

The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.

The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.

The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.

The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.

Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.

This paper investigates the key determinants affecting household spending for university degree in Vietnam. This paper serves as empirical evidence for policymakers to select appropriate factors to estimate financial needs for university students in Vietnam.

We employ an innovative variable selection approach with adaptive LASSO for Tobit Regression Model.

The results suggest income, region and ethnicity play significant roles in determining higher education expenditure at Vietnamese households. Gender-related indicators (such as gender of household head, student’s gender), distance to school, occupations, etc. are empirically insignificant.

This study proposes a data-driven method by interfering regulation and Tobit regression to understand the divergence in higher education spending. Especially, adaptive LASSO was first introduced to identify key determinants of higher education expenditure at household level in Vietnam. It hopes to tackle over-fitting problems of traditional OLS regression in previous literature.