TD Bank

Background Frailty is a dynamic state of vulnerability resulting from progressive functional decline and multimorbidity in patients with interstitial lung disease (ILD). The effect of lung transplantation (LTx) on frailty and its prognostic significance remains insufficiently understood. Methods This single-center cohort study aimed to characterize peri-transplant frailty trajectories, measured by the Clinical Frailty Scale (CFS), and determine their impact on long-term survival in patients with ILD undergoing LTx. CFS was assessed preoperatively, at 4-months and 5-years post-transplant. Patients were categorized as fit (CFS 1–3), vulnerable (CFS 4), or frail (CFS 5–9). Frailty change (ΔCFS) was classified as improved (ΔCFS ≤−1), unchanged (ΔCFS 0), or worsened (ΔCFS ≥+1). Survival was analyzed using Kaplan–Meier estimates and Cox proportional hazards models. Results The proportion of fit patients increased from 22.2% before to 87.5% 4 months, and 75.0% 5 years post-transplant. Frailty improved in 93.1%, remained unchanged in 2.8%, and worsened in 4.1% of patients. Median ΔCFS was −3 in frail, −1 in vulnerable, and 0 in pre-LTX fit patients. Prolonged ICU/hospital stay/ventilation was associated with reduced CFS recovery. Each one-point increase in ΔCFS was associated with a 1.78-fold higher mortality hazard (95%CI 1.34–2.35, p<0.001). Compared to those with improvement, patients with worsened frailty had a 40.7-fold higher mortality hazard (95%CI 10.1–163.6, p<0.001). Conclusions Peri-transplant CFS trajectory is associated with long-term survival in ILD, underscoring frailty as a modifiable risk factor and highlighting the need for systematic assessment and targeted interventions to optimize outcomes throughout the transplant course.

Malaysia’s Islamic banking sector faces intensifying competition, making a distinctive brand personality increasingly vital for attracting and retaining non-Muslim customers. At the same time, global financial disruptions have heightened consumer interest in ethical, transparent, and resilient financial systems, which are central to Islamic banking’s value proposition. However, despite this strength, Islamic banking’s value-based distinctiveness has not been effectively translated into a clear and relatable brand personality that resonates with non-Muslims. Addressing this gap forms the core purpose of this study, which proposes a conceptual framework that integrates brand personality dimensions from Aaker (1997), Garanti and Kissi (2019), and Jan and Shafiq (2021) to develop a more robust and culturally relevant brand personality for Islamic banks as a strategic bridge between Islamic banking identity and non-Muslim expectations. Guided by the Stimulus–Organism–Response (SOR) model, the integrated brand personality dimensions serve as stimuli expected to enhance brand trust, with the moderating role of digital financial literacy also considered. The study aims to provide insights for Islamic banks to strengthen their cross-faith appeal, reinforce trust, and sustain loyalty while remaining anchored in core Islamic values, and to attract global customers, including non-Muslims.

The latest study sheds light on distributed ledger technologies (DLTs) outside blockchain systems. The first section of this article introduces DLTs, focusing on blockchain as the main paradigm. It highlights three critical characteristics of blockchain: decentralisation, transparency, and security, and emphasises how blockchain is transforming various industries, including supply chain management and finance. Subsequently, the discussion shifts to new developments and approaches in the DLT space. It introduces next-generation ledgers designed to address traditional blockchains' scalability, energy efficiency, and interoperability challenges. The study delves into modern innovations that achieve higher transaction speeds and greater flexibility, such as hybrid models and directed acyclic graphs (DAGs). A significant portion is dedicated to how these advanced DLTs are used to transform sectors like healthcare government, secure patient data management, and enhance transparency and citizen participation. The article also addresses the challenges and ethical considerations of using these technologies. Finally, the paper predicts that DLTs will improve efficiency and innovation in industries outside blockchain technology. To maximise these new technologies' potential, research and interdisciplinary collaboration are essential.

This study evaluated the organizational health of an attached agency of the Department of Agriculture in the Philippines by developing an Organizational Health Behavioral Index (OHBI). The study used a descriptive quantitative design supplemented by qualitative analysis to capture 52 randomly selected employees' perceptions of their workplace environment. Data were collected using a standardized OHBI survey derived from Jaafari et al. (2023), which included aspects such as awareness, appreciation, interpersonal interactions, engagement, internal communication, employee persona, organizational culture, and employee voice. For quantitative data, descriptive and inferential statistical tools were used, whilst qualitative replies were analyzed thematically. The study found that the organization excelled in awareness (x̄=3.56) and relations (x̄=3.36), while internal communication (x̄=3.26) and appreciation (x̄=3.15) were regarded as successful but needed improvement. Employee engagement (x̄=2.80) was identified as the most essential concern, requiring focused actions. Qualitative findings identified teamwork and camaraderie as organizational strengths, whereas unfavorable cultural pockets, limited career progression prospects, and workplace politics emerged as recurrent issues. The estimated OHBI score was 74.75, indicating a generally healthy organizational climate with opportunities for improvement. Based on these findings, the report suggests implementing strategic initiatives such as formal employee appreciation programs, improved internal communications, career development frameworks, transparent promotion processes, leadership development programs, and culture-building workshops. These ideas attempt to promote an organizational culture that is cohesive, appreciative, and engagement-driven, with a focus on employee well-being and professional development.

Purpose This study aims to examine the factors influencing customers’ continuous intention to use chatbots in Jordan’s telecommunications industry by integrating the Stimulus-Organism-Response (S-O-R) framework with the technology acceptance model. Design/methodology/approach A quantitative research design was employed to examine the relationships between stimulus factors and users' continuous intention to use telecom chatbots. Data were collected from 331 users of Jordan’s 3 major telecom providers through an online questionnaire and analysed using structural equation modelling. Findings The results show that perceived responsiveness and usefulness significantly influence customers’ continuous intention to use chatbots in the telecommunications industry. Moreover, trust fully mediates the relationship between anthropomorphism and continuous intention and partially mediates the link between responsiveness and continuous intention, highlighting its central role in fostering sustained use. In contrast, perceived enjoyment was not a significant predictor. Additionally, age was found to moderate the relationship between perceived usefulness and continuous intention, with younger users demonstrating a stronger intention to continue using chatbots. Practical implications The findings highlight that human–chatbot interaction is shaped by both logical assessment and emotional processes. This study provides insights for designing and implementing chatbots that drive economic efficiencies and social inclusion in the telecommunications industry. Originality/value The study highlights the critical role of functional performance and trust in driving users’ continuous intention to use chatbots, particularly in emerging markets where artificial intelligence is still gaining user acceptance. These insights contribute to the growing body of literature on chatbot usage and sustainable digital services.

Abstract Stars primarily form in galactic spiral arms within dense, filamentary molecular clouds. The largest and most elongated of these molecular clouds are referred to as “bones,” which are massive, velocity-coherent filaments (lengths ∼20 to >100 pc, widths ∼1–2 pc) that run approximately parallel and in close proximity to the Galactic plane. While these bones have been generally well characterized, the importance and structure of their magnetic fields (B-fields) remain largely unconstrained. Through the Stratospheric Observatory for Infrared Astronomy Legacy program FIlaments Extremely Long and Dark: a Magnetic Polarization Survey (FIELDMAPS), we mapped the B-fields of 10 bones in the Milky Way. We found that their B-fields are varied, with no single preferred alignment along the entire spine of the bones. At higher column densities, the spines of the bones are more likely to align perpendicularly to the B-fields, although this is not ubiquitous, and the alignment shows no strong correlation with the locations of identified young stellar objects. We estimated the B-field strengths across the bones and found them to be ∼30–150 μ G at parsec scales. Despite the generally low virial parameters, the B-fields are strong compared to the local gravity, suggesting that B-fields play a significant role in resisting global collapse. Moreover, the B-fields may slow and guide gas flow during dissipation. Recent star formation within the bones may be due to high-density pockets at smaller scales, which could have formed before or simultaneously with the bones.

As digital ecosystems grow in complexity and scale, fraudsters are increasingly leveraging automation, adversarial manipulation, and synthetic identities to evade detection. Traditional fraud prevention systems largely rule-based or dependent on shallow machine learning struggle to keep pace with these evolving threats. In this work, we propose an adaptive deep learning framework for intelligent fraud prevention, capable of ingesting multi-source data, modeling contextual relationships, estimating uncertainty, and continuously learning from new fraud patterns. The framework integrates a hybrid encoder (CNN + BiLSTM), a Graph Neural Network for relational fraud detection, and a Bayesian uncertainty-aware classification head. It also incorporates a continual learning loop to adapt to zero-day fraud strategies. Extensive experiments on benchmark datasets such as IEEE-CIS and PaySim demonstrate that our framework outperforms existing methods in terms of accuracy, robustness to adversarial attacks, and inference latency, making it highly suitable for real-world deployment in financial, telecom, and ecommerce platforms.