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This study investigates the antecedents of supply chain resilience of small and medium-sized enterprises (SMEs) and the effects of government assistance and disruption intensity in long-term disruptions.

This study collected data from 626 SMEs in Australia in 2022 and analysed data using partial least squares structural equation modelling.

The study empirically confirms that digital capabilities, prior experience in disruptions, supplier proximity and relationships are antecedents of supply chain resilience of SMEs, with supply chain robustness as a mediator. It further confirms that SMEs' access to government assistance positively moderates the relationship between digital capabilities and supply chain robustness. The disruption intensity moderates the relationships between supplier proximity and supply chain robustness with supply chain resilience. Severe disruptions weaken the effects of prior disruption experiences and supplier relationships on supply chain resilience.

The findings inform SME practitioners of the importance of building supply chain robustness, leveraging their prior experience, supplier proximity and relationships and capabilities and flexibility for dynamic supply chain structures when disruptions are intense.

The novelty of our study is the use of the Contingent Resource-Based View to understand the effects of firm and supply chain-level antecedents on supply chain robustness and resilience, considering the contextual contingencies of disruption intensity and government assistance. The focus on long-term disruptions extends the conventional supply chain resilience studies on supply and demand disruptions of small scale. We also explore the firm-level effects of government assistance, which extends the commonly tested economic-level effects. Furthermore, we investigate supply chain robustness and resilience as different but connected constructs, deviating from common approaches. The finding that the relationship between digital capabilities and supply chain robustness, not the relationship between digital capabilities and supply chain resilience, becomes stronger with higher access to government support shows the importance of this approach to investigating specific effects.

Despite the proliferation of cyberthreats upon the supply chain (SC) at large, knowledge on SC cybersecurity is scarce and predominantly conceptual or descriptive. Addressing this gap, this research examines the effect of SC cyber risk management strategies on integration decisions for cybersecurity (with suppliers, customers, and internally) to enhance the SC’s cyber resilience and robustness.

A research model grounded in the supply chain risk management (SCRM) literature, with roots in the Dynamic Capabilities View and the Relational View, was developed. Survey responses of 388 SC managers at US manufacturers were obtained to test the model.

An impact of SC cyber risk management strategies on internal cyber integration was detected, which in turn impacted external cyber integration with both suppliers and customers. Further, a positive effect of internal and customer cyber integration on both cyber resilience and robustness was found, while cyber integration with suppliers impacted neither.

Industry practitioners may adapt certain risk management and integration strategies to enhance the cybersecurity posture of their SCs.

This research bridges between the established domain of SCRM and the emergent field of SC cybersecurity by forming and testing novel relationships between SCRM-rooted constructs tailored to an SC cyber risks context.

In light of the recently experienced systemic shocks (the COVID-19 pandemic and the war in Ukraine), we investigate supply chain robustness. We aim to understand the potential consequences of uncertain events or adversary’s action on critical supplies in the Alliance.

We leverage a parsimonious supply chain model and investigate the relationship between upstream supplier concentration/diversification and the supply chain’s robustness (survival probability) in the presence of uncertain systemic shocks. In several scenarios of shock events, we simulate alternative input sourcing strategies in the presence of uncertainty.

A firm-level cost-focused optimisation may lead all upstream suppliers to concentrate in one location, which – when subsequently hit by a shock – would result in a disruption of the entire supply chain. A chain-level forward-looking optimisation diversifies the upstream supplier location and sourcing decisions. As a result, the supply chain’s survival probability is maximised, and critical supplies will continue even under the most demanding circumstances.

Our findings encourage political and military decision makers to enhance upstream supply chain robustness in critical and strategic sectors, such as the diversification of nitrocellulose supplies currently sourced almost exclusively from China by European gunpowder manufacturers.

Our findings have direct recommendations to supply chain downstream decision makers and to the government’s policy choices. Since global supply chain (GSC) disruptions in critical sectors may have catastrophic impacts on social welfare and the probability of shocks such as COVID-19 and Russia’s war may not be known even approximately, robust decision rules seem to be the appropriate tools for policymaking in critical and strategic sectors such as energy supplies, food and water, communication and defence. A robust supply chain is one in which the survival probability is maximised, which we show in a central planner strategy’s simulations.

The paper shows formally why a market-based global input sourcing strategy may be efficient from an individual firm’s perspective but may be suboptimal from a societal resilience perspective.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

This study aims to review the computational framework of SDGs in India, so that a mid-course correction can be contemplated.

This study deploys, inter alia, econometric analysis to probe the robustness of indicators of SDG India Index 3.0. Methodologically, the study intensively probes the robustness of SDG India index and extensively refers to the global SDG indexes for cross-checking.

Though the three editions of SDGI index mark significant efforts taken towards measuring the progress of SDGs in India, the paradigm suffers from the problem of too many indicators chasing only few targets, quantitative and qualitative issues with indicators, vintage pollution, partial coverage of targets and robustness issues.

This study has the limitation that it could not check the robustness of SDG scores with different weights assigned to indicators and future researchers can take up that interesting assignment.

Since measuring the SDG progress through SDG index is a global endeavour, the findings of this study are important for almost all countries of the world, as it is still not too late to do mid-course correction because it is not the measurement that matters at the end of the day, rather it is the outcome of sustainable development that every country cares about.

The obfuscation of layers of SDG index in crafty, glossy and power-point-presentation-oriented SDG reports should get the reality check through such review of the computational framework of SDGs.

This is the first study that unpacks the layers of SDG index computation in general and comprehensively reviews the Indian SDG indexing method in particular.

Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.

This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.

The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.

This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.

This research endeavors to assess the influence of financial shared service centers (FSSCs) on the quality of accounting information within China’s A-share listed companies. Using a multi-period difference-in-differences (DID) model, the study aims to empirically examine the correlation between the adoption of FSSCs and the quality of accounting information.

The study uses a robust methodology to evaluate the relationship between FSSCs and accounting information quality (AIQ). Leveraging the established FSSCs within China’s A-share listed companies as the treatment group, this research adopts a multi-period DID model. This approach enables a rigorous empirical examination of the influence exerted by FSSCs on the overall quality of accounting information.

The present study delves into the impact of FSSCs on AIQ and conducts empirical analysis using data from Chinese A-share listed companies between 2004 and 2021. The findings substantiate that: FSSCs significantly bolster the quality of accounting information, a conclusion retained even after robustness tests. Specifically, FSSCs exhibit a positive correlation with the comparability, timeliness and disclosure quality of accounting information while demonstrating no significant influence on relevance, robustness and reliability factors.

First, the analysis primarily rests upon data from Chinese A-share listed companies between 2004 and 2021, potentially constraining the generalizability of findings across diverse contexts. Second, despite controlling for various factors, unobserved variables or external factors not encompassed in the model might influence the relationship between FSSCs and AIQ. Additionally, the study’s reliance solely on quantitative data confines exploration into qualitative aspects that might offer a more comprehensive understanding of FSSCs’ impact on AIQ.

This paper establishes a nuanced connection between FSSC operations and AIQ, furnishing direct empirical evidence for their economic implications and propounding a novel avenue for augmenting AIQ. And, it furnishes guidance for forthcoming FSSC development, accentuating the necessity of harnessing information technology to enhance the relevance, reliability and robustness of accounting information.

Majority of prior empirical studies assessing AIQ have focused on singular indicators, lacking a comprehensive depiction of its overall level. To address this gap, this paper pioneers the construction of a comprehensive index for AIQ, providing a holistic representation of its level. Furthermore, this study stands as the inaugural investigation into the relationship between China’s A-share listed firms’ FSSCs and the quality of accounting information.

The present study aims to investigate the impact of internal control effectiveness on supply chain management efficiency (SCME) and capital allocation efficiency for companies listed in the Tehran Stock Exchange (TSE). In addition, it investigates the mediating role of supply chain management efficiency in the relationship between internal controls and capital allocation efficiency.

The data about 191 companies in 2014–2022 were examined. The sales per inventory ratio was used to calculate SCME. The present study also applied the Generalized Method of Moments (GMM) for endogeneity concerns.

The results showed that internal control effectiveness has a significant positive effect on SCME. Moreover, internal control effectiveness and SCME significantly positively affect capital allocation efficiency. SCME has a mediating role in the relationship between internal control effectiveness and capital allocation efficiency. These findings remained robust even after several robustness tests. In addition, this study tested the results' robustness by dividing data into the pre-COVID-19 and post-COVID-19 years. The previous results were also confirmed according to the robustness test of COVID-19.

Challenges in the supply chain often hinder capital allocation efficiency. In addition, enterprises should try to establish strong internal controls to ensure SCME. Therefore, the relationship between internal control effectiveness, SCME and capital allocation efficiency is complex and underscores the importance of robust internal controls in optimizing resource allocation within organizations. Interestingly, this topic has not been extensively researched in accounting and business research, and there is a lack of empirical evidence on these effects. Consequently, this study aims to fill the gap and identify potential opportunities for new research directions.

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.