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The extensive use of mobile money (MM) has been widely recognized as a digital engine of socioeconomic development in sub-Saharan Africa (SSA). This paper aims to focus on the effects of MM use and stockouts on informal microenterprise performance and investigate whether MM use mitigates the relationship between stockouts and firm performance.

This study utilizes firm-level data from the latest World Bank Informal Sector Enterprise Surveys across six SSA countries. We employ instrumental variable-adjusted and propensity score-weighted regressions to investigate the buffering effect of MM use.

We find a significantly positive effect of MM use and a significantly negative impact of stockouts on informal microenterprise performance. Importantly, we establish that MM use attenuates the negative impact of stockouts on firm performance. We further document that the attenuating effect of MM use is more profound for firms using MM for transactions with supply chain partners, located in communities with high MM use rates, and operating in the retail industry.

Our research generates important managerial and policy implications. Future policies should capitalize on MM to foster an effective financial ecosystem in which informal microenterprises can survive and grow, thereby deepening their contributions to sustainable development.

Whereas the business benefits of MM among small, medium and large firms are well-documented, the role of MM use on informal microenterprise performance is less understood. This study fills the research gap in the literature by focusing on the influence of MM use on the relationships between informal microenterprise operations and performance.

This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities.

This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks.

The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models.

This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

The proposed model aims to tackle the data quality issues in multivariate time series caused by missing values. It preserves data set integrity by accurately imputing missing data, ensuring reliable analysis outcomes.

The Conv-DMSA model employs a combination of self-attention mechanisms and convolutional networks to handle the complexities of multivariate time series data. The convolutional network is adept at learning features across uneven time intervals through an imputation feature map, while the Diagonal Mask Self-Attention (DMSA) block is specifically designed to capture time dependencies and feature correlations. This dual approach allows the model to effectively address the temporal imbalance, feature correlation and time dependency challenges that are often overlooked in traditional imputation models.

Extensive experiments conducted on two public data sets and a real project data set have demonstrated the adaptability and effectiveness of the Conv-DMSA model for imputing missing data. The model outperforms baseline methods by significantly reducing the Root Mean Square Error (RMSE) metric, showcasing its superior performance. Specifically, Conv-DMSA has been found to reduce RMSE by 37.2% to 63.87% compared to other models, indicating its enhanced accuracy and efficiency in handling missing data in multivariate time series.

The Conv-DMSA model introduces a unique combination of convolutional networks and self-attention mechanisms to the field of missing data imputation. Its innovative use of a diagonal mask within the self-attention block allows for a more nuanced understanding of the data’s temporal and relational aspects. This novel approach not only addresses the existing shortcomings of conventional imputation methods but also sets a new standard for handling missing data in complex, multivariate time series data sets. The model’s superior performance and its capacity to adapt to varying levels of missing data make it a significant contribution to the field.

This study aims to examine the risk-resistant role of environmental, social and governance (ESG) performance in the capital market, focusing on an organizational standpoint. Furthermore, it aims to offer management decision advice to companies seeking protection against stock market risks. Conclusions obtained through this research have the potential to enrich the economic consequences of ESG performance, provide practical implications for enhancing corporate ESG performance, improving corporate information quality and stabilizing capital market development.

Based on the data of Chinese A-share listed companies from 2009 to 2020, this study examines the risk-resistant function of ESG performance in the capital market. The impact of ESG performance on management behavior is analyzed from the perspective of organizational management and the three mechanisms of pre-event, during the event and post-event.

This paper demonstrates that companies that effectively implement ESG practices are capable of effectively mitigating risks associated with stock price crashes. Heterogeneity analysis reveals that the inhibitory effect of ESG performance on stock price crash risk is more pronounced in nonstate-owned enterprises and enterprises with higher levels of marketization. After controlling for issues such as endogeneity, the conclusions of this paper are still valid. The mechanism analysis indicates that ESG performance reduces the risk of stock price crash through three paths of organizational management: pre-event, during the event and post-event. That is, ESG performance plays the role of restraining managers’ opportunistic behavior, reducing information asymmetry and boosting investor sentiment.

This paper provides new insights into the relationship between ESG performance and stock price crash risk from an organizational management perspective. This study establishes three impact mechanisms (governance effect, information effect and insurance effect), offering a theoretical basis for strategic corporate decisions of risk management. Additionally, it comprehensively examines the contextual differences in the role of ESG performance, shedding light on the specific domains where ESG practices are influential. These findings offer valuable insights for promoting stable development in the capital market and fostering the healthy growth of the real economy.

The purpose of this study is to develop a transient wheel–rail rolling contact model to primarily investigate the rail damage under wet condition when the train passes through the welded joints.

The impact force induced by welded joints is obtained through vehicle–track coupling dynamics. The normal and tangential wheel–rail contact pressures were solved by elastohydrodynamic lubrication (EHL) theory and simplified third-body layer theory, respectively. Then, the obtained tangential pressure and normal pressure were applied to the finite element model as moving loads, simulating cyclic loading. Finally, the shakedown map and critical plane method were used to predict rolling contact fatigue (RCF) and the initiation of fatigue cracks.

The results indicate that RCF will occur and fatigue cracks are more prone to appear on the subsurface of the rail, specifically around 2.7 mm below the rail surface in the vicinity of the welded joint and its heat-affected zone.

The cosimulation of numerical model and finite element model was implemented. The influence of surface roughness and fluids was considered. In this model, the normal and tangential wheel–rail contact pressure, the stress and strain and the rail fatigue cracks were obtained under a rail-welded joint excitation.

The purpose of this study is to develop a 3D wheel-rail adhesion model under wet condition, which considers the generated surface roughness topography and the traditional braking procedure for high-speed trains.

Wheel-rail adhesion has an important effect on the braking ability of railway vehicle. Based on the deterministic mixed lubrication approach, the model was solved to get the adhesion characteristics of the train during braking. The elastic deformation was calculated with the discrete convolution and fast Fourier transform method. The simulation results of adhesion coefficient were compared with the experimental values. The wheel-rail adhesion characteristics of train braking at several different initial speeds were investigated. The effects of the time-step length and roughness orientation on the contact load ratio were also discussed.

The results show that the adhesion coefficient of the numerical model is in good agreement with the experimental results. At the instant of braking, the adhesion coefficient drops to a lower adhesion level, the value of adhesion coefficient is lower than 0.06, especially at a higher speed (200, 300 and 400 km/h).

It can provide a better understanding of the low adhesion phenomenon of train braking under wet condition.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2023-0040/

The purpose of this paper is to study the behavior of smoke flow in building fires and optimize the design of smoke control systems.

A total of 435 3-D fire simulations were conducted through NIST fire dynamics simulator to analyze thermal behavior of combined buoyancy-induced and pressure-driven smoke flow in complex vertical shafts, under consideration of influence of heat release rate (HRR) and locations of heat sources. This influence was evaluated through neutral pressure plane (NPP), which is a critical plane depicting the flow velocity distributions. Hot smoke flows out of shafts beyond the NPP and cold air flows into shafts below the NPP.

Numerical simulation results show that HRR of heat source has little influence on NPP, while location of heat source can make a significant difference to NPP, particularly in cases of multi-heat source. Identifying the location of NPP helps to develop a more effective way to control the smoke with less energy consumption. Through putting an emphasis on smoke exhausting beyond the NPP and air supplying below the NPP, the smoke control systems can make the best use of energy.

Because of the chosen research approach, the research results may need to be tested by further experiments.

The paper includes implications for the optimization of smoke control systems design in buildings.

This paper fulfills an identified need to research the behavior of hot smoke in building fires and optimize the design of smoke control systems.

The purpose of this paper is to study the behavior of smoke flow in a typical high-rise residential building fire in six common smoke control systems.

The pressure, temperature and CO₂ concentration were used to trace the motion of turbulent smoke flow through CFD.

It is found that the hot smoke could rise up and spread into the indoor space on the upper floors through the staircase. When the pressure in the evacuation staircase is higher, it would be more difficult for the smoke to enter the staircase and transport vertically. On the other hand, the smoke would soon transport to the indoor space on the upper floors horizontally. During this process, the smoke shows a more disorder horizontal transport under the sole effect of thermal buoyancy than the co-existence of thermal buoyancy and the air inlet.

Because of the chosen research approach, the research results may need to be tested by further experiments.

The paper includes implications for the design of smoke control systems and evacuation in a building fire.

This paper fulfils an identified need to study the behavior of smoke in a fire and optimize the design of smoke control systems.

This paper scrutinizes exergy loss and hydrothermal analysis of Linear Fresnel Reflector (LFR) unit by means of FLUENT. Several mirrors were used to guide the solar radiation inside the receiver, which has parabolic shape. Radiation model was used to simulate radiation mode.

Heat losses from receiver should be minimized to reach the optimized design. Outputs were summarized as contours of incident radiation, isotherm and streamline. Outputs were classified in terms of contours and plots to depict the influence of temperature of hot wall, wind velocity and configurations on performance of Linear Fresnel Reflector (LFR) based on thermal and exergy treatment. Four arrangements for LFR units are considered and all of them have same height.

Greatest Nu and E_x can be obtained for case D due to the highest heat loss from hot wall. Share of radiative heat flux relative to total heat flux is about 94% for case D. In case D when T_r = 0.388, As h_ext rises from 5 to 20, Nu_total enhances about 11.42% when T_r = 0.388. By selecting case D instead of case A, E_x rises about 16.14% for lowest T_r. Nu_total and E_x of case D augment by 3.65 and 6.23 times with rise of T_r when h_ext = 5. To evaluate the thermal performance (η_th) of system, absorber pipe was inserted below the parabolic reflector and 12 mirrors were used above the ground. The outputs revealed that η_th decreases about 14.31% and 2.54% with augment of T_in and Q if other factors are minimum.

This paper scrutinizes exergy loss and hydrothermal analysis of LFR unit by means of finite volume method. Several mirror used to guide the solar radiation inside the receiver, which has parabolic shape. DO model was used to simulate radiation mode. Heat losses from receiver should be minimized to reach the optimized design. Outputs were summarized as contours of incident radiation, isotherm and streamline.