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  Hybrid Machine Learning–Econometric Framework for Financial Distress Scoring: Evidence from German Manufacturing Firms

  (MDPI, 2026-02-10)

  Jegerson, Devid

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  Mertzanis, Charilaos

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  Khan, Mehmood

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  Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly contributes to addressing these issues by providing data-driven analytical tools that improve the assessment and monitoring of firms’ financial position. However, in the literature, we have not found any paper that uses machine learning (ML) algorithms to assess the financial distress of German manufacturing firms, highlighting methodological and sectoral gaps that need to be bridged. Therefore, this study aims to develop an econometric and ML-based financial distress scoring model for German manufacturing firms by estimating contemporaneous Altman Z-scores that provide better insights into the financial distress determinants, enabling better financial management. The econometric findings revealed that the regression model has an adjusted R-squared value of 86%, confirming that the selected firm-specific and macroeconomic factors play a substantial role in explaining financial distress. The findings recommend that German manufacturing businesses retain more earnings rather than distributing them as dividends, while reducing their debt in capital structures to enhance financial stability. Moreover, the ML results found that Gradient Boosting and Random Forest have the highest accuracy scores among the ML methods, suggesting that these models provide strong capability for assessing financial distress and supporting more effective financial risk management, allowing firms to effectively respond to the threats of a dynamic environment and thereby better support the growth of the German and European economies. Keywords financial distress, machine learning, Altman Z-score, German manufacturing

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  Serum ornithine to arginine ratio as a novel diagnostic test for rheumatoid arthritis in women

  (cell.com, 2026-03)

  Al-Adwan, Safwan M.

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  Al-Qaisi, Talal S.

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  Oriquat, Ghaleb A.

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  Nsairat, Hamdi

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  ETAL..

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  Finding specific serum biomarkers linked to rheumatoid arthritis (RA) can help us understand the course of the disease, and the prognosis. Here, we aimed to explore the potential use of arginine, ornithine, tryptophan, citrulline, serotonin and several other biochemical markers for early diagnosis of rheumatoid arthritis (RA). We examined serum samples from 30 controls and 60 RA patients to achieve this goal. According to our findings, there was a statistically significant difference in the serum levels of ornithine and arginine between RA patients and controls, with higher ornithine and lower arginine in RA patients compared to controls. Furthermore, we found that only patients with high disease activity index had considerably greater levels of serotonin. Additionally, we found that using arginine alone can predict RA disease with 96.7% sensitivity and 80.8 % specificity, while ornithine can predict RA disease with 100% sensitivity and 66.7% specificity. Interestingly, the ornithine to arginine ratio (OR/AR) could identify people with RA disease with 100% sensitivity and 83.3% specificity and this clear discrimination is not affected by the disease index or duration. Hence, RA patients can be distinguished using the ornithine to arginine ratio as a biomarker, which has higher specificity than each analyte alone. Our results can undoubtedly serve as a foundation for additional research and multicenter studies in the future to support accurate therapeutic management strategies for RA patients. Keywords Rheumatoid arthritis, DAS score , Ornithine , Arginine , Serotonin

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  The impact of electronic service quality on customer satisfaction and loyalty

  (SSRN, 2026-02-19)

  Helal, Tarig Osman Abdallah

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  Alnor, Nasareldeen Hamed Ahmed

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  Alruwali, Shatha Salem

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  Abdallah, Abderhim Elshazali Yahia

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  Yahya, Hamza Abdallah Abdalrhman

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  In recent years, e-commerce has become increasingly common. Many studies have shown that improving internet quality, enhancing purchasing processes, and effectively retaining customers significantly contribute to customer satisfaction and loyalty. This study aims to examine the relationship between the quality of electronic services and customer satisfaction and loyalty. The researchers employed descriptive and case study methods and distributed 122 questionnaires to a sample of Sudanese bank customers. The results indicate a statistically significant positive relationship between performance and customer loyalty. A significant positive effect was also found between fulfillment of requirements and customer satisfaction, as well as between fulfillment of requirements and customer loyalty. In addition, system accessibility had a statistically significant positive impact on both customer satisfaction and customer loyalty. Privacy was found to have a statistically significant positive relationship with customer satisfaction and customer loyalty. Overall, customer satisfaction and loyalty improved significantly depending on the availability of quality dimensions in electronic services. Keywords Electronic Service Quality, Customer Satisfaction, Customer Loyalty, System Accessibility, Privacy

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  Factors influencing m-government adoption in the developing world: a UTAUT-based model integrating trust, risk and trendiness

  (Emerald, 2025-11-21)

  Qatanani, Noor Othman

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  Alghababsheh, Mohammad

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  Aburayya, Ahmad

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  Nasaj, Mohamed

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  Purpose This study aims to examine the factors influencing behavioural intention to use mobile government (m-government) in the developing world from a citizens’ perspective. For this purpose, it extends the unified theory of acceptance and use of technology (UTAUT) (i.e. performance expectancy, effort expectancy, social influence and facilitating conditions) with perceived trust, risk and trendiness. Design/methodology/approach A survey was developed and administered online to citizens of the country of Jordan. The 445 responses received were analysed using the partial least squares structural equation modelling technique. Findings This study found support for the UTAUT’s positive prediction of only performance expectancy and facilitating conditions on citizens’ intention to use m-government. They also found that perceived trust and perceived trendiness positively drive the intention to use m-government, while perceived risk negatively does. Research limitations/implications This study examined citizen’s intention to use m-government in general terms in a single developing country context. It could be extended by exploring citizen’s continuous intention behaviour, focussing on specific m-government services and conducting a cross-country empirical examination. Social implications Understanding the factors that influence m-government adoption can help in increasing public service accessibility, driving more citizens’ participation and enhancing sustainability. Originality/value By empirically considering perceived trust and risk along the UTAUT’s factors, this study provides theoretical refinement and contextual insights regarding the factors that influence the adoption of m-government services in the developing world. Additionally, by integrating perceived trendiness, this study introduces a socio-psychological dimension to UTAUT to better understand m-government adoption. Keywords E-government, M-government, Public service delivery, Risk, Trendiness, Trust

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  Large Language Model-Empowered Wireless Networks: Fundamentals, Architecture, and Challenges

  (IEEE, 2026-02-03)

  Khan, Latif U.

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  Guizani, Maher

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  Muhaidat, Sami

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  Hong, Choong Seon

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  The rapid advancement of wireless networks has resulted in numerous challenges stemming from their extensive demands for quality of service towards innovative quality of experience metrics (e.g., user-defined metrics in terms of sense of physical experience for haptics applications). In the meantime, large language models (LLMs) emerged as promising solutions for many difficult and complex applications/tasks. These lead to a notion of the integration of LLMs and wireless networks. However, this integration is challenging and needs careful attention in design. Therefore, in this article, we present a notion of rational wireless networks powered by telecom LLMs, namely, LLM-native wireless systems. We provide fundamentals, a vision, and a case study of a distributed implementation of LLM-native wireless systems. In the case study, we propose a solution based on double deep Q-learning (DDQN) that outperforms existing DDQN solutions. Finally, we provide open challenges. Keywords Internet of Things, large language models, deep reinforcement learning, convex optimization

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