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We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.

Market sentiment has shown to influence housing prices in the global north, but in emerging economies, the nexus is rare to chance on in the current state of science for policy direction. More importantly in the recent decade where policymakers are yet to conclude on the myriad of factors confronting the housing market in sub-Saharan Africa inhibiting affordability. This paper therefore examines the impact of market sentiment on house prices in South Africa.

The study used the Autoregressive Distributed Lag (ARDL) approach with quarterly data spanning from 2005Q1 to 2020Q4.

In all, it was established that market sentiment plays a minimal role in the property market in South Africa. But there was enough evidence of cointegration from the bound test between sentiment and house prices. Nevertheless, the lag values of sentiment pointed to a rise in house prices. Exchange rate volatilities and inflation had a statistically significant effect on prices in both the long and short term, respectively.

Policymakers could still monitor market sentiment in the housing market due to the strong chemistry between house prices and sentiment, as evidenced from the bound test, but focus on economic fundamentals as the main policy tool for house price reduction.

The findings and the creation of the sentiment index make an invaluable contribution to the paper and add to the paucity of literature on the study of market sentiment in the housing market.

This paper empirically investigates the relationship between financial availability and crime by measuring it across five dimensions: banking, securities, insurance, private lending and digital inclusive finance.

The study utilizes 2011–2017 data from prefecture-level cities as a representative sample. Moreover, these findings remain robust after addressing endogeneity through the use of the historical distance between cities and the railroad network as an instrumental variable.

The findings demonstrate a significant negative relationship between financial accessibility and crime rates. Heterogeneity exists in the inhibitory effect of different types of financial accessibility on crime, with banking finance exhibiting a stronger inhibitory effect compared to private lending. Areas affected by natural disasters and infectious diseases exhibit a stronger inhibitory effect of financial accessibility on crime rates, particularly in areas with severe shocks of natural disasters and epidemics. This effect is attributed to the low financing threshold and easy access to private lending, which plays a more effective role than bank finance when people face extreme risks.

There should be stricter regulations imposed on private lending markets and the introduction of more rational legislation aimed at guiding a healthy development within these markets; such measures serve as effective and complementary means for individuals from all walks of life to access credit financing.

The regulation of financial resources by the government should always prioritize ensuring the accessibility of financial policies to cater to the needs of the majority population.

This study is for the first time in an emerging economy context, the causal relationship between financial accessibility and crime. To provide a more comprehensive measure of financial accessibility in a region, this paper proposes a five-dimensional methodology.

This study attempts to assess the role that institutional quality (IQ) plays in influencing inflows and outflows of Foreign Direct Investment (FDI) for BRICS nations as burgeoning FDI is flowing into and out of these countries. Moreover, this paper explores the impact of individual governance indicators separately on the FDI flows.

The study analyses this nexus for these emerging economies for the period 1996–2019 using autoregressive distributed lag technique.

The study indicates a significant and positive coefficient for IQ in India and South Africa, suggesting that improving IQ would enhance the IFDI. However, for outward FDI (OFDI)–IQ linkage, the results show a negatively significant impact of IQ on OFDI for Brazil and Russia. Additionally, the authors observe control of corruption as a significant institutional component for attracting inward FDI for Brazil, India and South Africa, whereas it is an insignificant factor for Russia and China. Further, the authors notably find that upgrading the governance indicators will decrease the level of OFDI for Brazil, Russia, China and South Africa. On the contrary, findings suggest that improving the IQ will foster the OFDI for India.

This study uses time-series analysis instead of cross-country analysis (used extensively in literature), avoiding heterogeneity. Further, this study explores the IFDI–IQ link for BRICS nations, which are captivating a significant chunk of IFDI, and still not given much attention in the extant literature. Moreover, the authors identify the impact of IQ on the OFDI, neglected by the existing studies.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

The purpose of this study is to explore the interplay among self-organization, networks and sustainable innovations within microfinance institutions (MFIs) and to examine the extent to which organizational resilience plays a significant role in shaping these dynamics as a mediator.

This paper adopted a cross-sectional research design combined with analytical and descriptive approach to collect the data. Smart partial least squares structural equation modeling (PLS-SEM) was used to construct the measurement model and structural equation model to test the mediating effect under this study.

The results revealed that organizational resilience is a significant mediator in the relationship between self-organization, networks and sustainable innovations among microfinance institutions in Uganda.

The data for this study were collected only from microfinance institutions in Uganda. Future studies may collect data from other formal financial institutions like commercial banks and credit institutions to test the mediating effect of organizational resilience. More still, the study adopted only a single approach of using a questionnaire. However, future research through interviews may be desirable. Likewise this study was cross-sectional in nature. Therefore, a longitudinal study may be useful in future while investigating the mediating role of organizational resilience traversing over a long time frame.

A possible implication is that microfinance institutions which desire to have sustainable innovative solutions for their business operations in disruptive circumstances may need to scrutinize their capacity to be resilient and self-organize.

Microfinance institutions play a great role to the underserved clients. Thus, for each to re-organize to be able to provide services that meet users’ needs, without physical products so as to ensure long-term financial and social welfare combined with the ability to bounce back and adapt in times of economic downturn to avoid mission adrift.

While most studies have been carried out on organizational resilience, this paper takes center stage and is the first to test the mediating role of organizational resilience in the relationship between self-organization, networks and sustainable innovations, especially in microfinance institutions in Uganda. This paper generates strong evidence and contributes to the powerful influence of organizational resilience in enhancing the level of sustainable innovations based on self-organization and networks.

The purpose of this paper is to study the aerodynamic characteristics of Ahmed body in longitudinal and lateral platoons under crosswind by computational fluid dynamics simulation. It helps to improve the aerodynamic characteristics of vehicles by providing theoretical basis and engineering direction for the development and progress of intelligent transportation.

A two-car platoon model is used to compare with the experiment to prove the accuracy of the simulation method. The simplified Ahmed body model and the Reynolds Averaged N-S equation method are used to study the aerodynamic characteristics of vehicles at different distances under cross-winds.

When the longitudinal distance x/L = 0.25, the drag coefficients of the middle and trailing cars at β = 30° are improved by about 272% and 160% compared with β = 10°. The side force coefficients of the middle and trailing cars are increased by 50% and 62%. When the lateral distance y/W = 0.25, the side force coefficients of left and middle cars at β = 30° are reduced by 38% and 37.5% compared with β = 10°. However, the side force coefficient of the right car are increased by about 84.3%.

Most of the researches focus on the overtaking process, and there are few researches on the neat lateral platoon. The innovation of this paper is that in addition to studying the aerodynamic characteristics of longitudinal driving, the aerodynamic characteristics of neat lateral driving are also studied, and crosswind conditions are added. The authors hope to contribute to the development of intelligent transportation.

This study aims to measure the absolute impact of corporate citizenship actions on the operable elements of the public image by developing an adequate and parsimonious instrument.

Both qualitative and quantitative approaches are used, where initially a literature review is systematized, then related statements are created, examined and confirmed. Altogether, 296 responses have been tested at discrete points, allowing for a temporal split-up of observations, where the first 148 forms have been used for exploratory factor analysis and the remaining 148 for confirmatory factor analysis.

The results of exploratory factor analysis revealed that the proposed instrument contains 13 items under three components: corporate citizenship and public affiliation; corporate citizenship and public allegiance; and corporate citizenship and public accomplishment. Subsequently, confirmatory factor analysis findings attest to the completeness, robustness and fitness of the same.

This experiment would serve as an inducement that would bridge the theoretical and empirical gap between corporate citizenship and public image by imparting an extensive perspective.

Perhaps on account of the lack of an inclusive instrument, the holistic view of corporate citizenship has secured quite less empirical attention so far, particularly from the perception of that group of stakeholders who manifest wholeness. This study, thus by making a ground-breaking methodological endeavor with the conceptually established construct of public image, would abet in shaping a new class of “wholistic”, i.e. whole and holistic corporations.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.