Search Results (140)

As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m. Full article

Pixel-level semantic segmentation plays an essential role in coastal wetland monitoring using multispectral remote sensing imagery. However, accurate mapping remains challenging due to spectral confusion among heterogeneous land-cover types, fragmented spatial structures, and pronounced class imbalance. Based on the situation, we used the original SegFormer as the basic framework and developed an improved framework to better suit the characteristics of coastal wetland scenes. Prior to the encoder, we introduced a Spectral-Aware Embedding (SAE) module to strengthen inter-band feature representation through spectral projection and adaptive channel weighting. In the decoder, we constructed a Wetland Boundary-Refined Decoder (WBRD), utilizing a dual-path refinement strategy to capture fine-scale textures and a multi-scale boundary attention mechanism to enhance the delineation of irregular boundaries. Additionally, we incorporated a Wetland Imbalance Loss (WIL) during training to moderate the influence of dominant classes. In this article, we evaluated our framework on the Yan14 dataset. The results showcased the framework’s effectiveness, improving segmentation accuracy and boundary fidelity, particularly for rare and narrow wetland categories, while maintaining reasonable computational efficiency. Full article

Smart-city last-mile rail access, referred to in this entry simply as last-mile access, captures how travelers connect to and from rail stations during the first or last leg of a journey. It encompasses both the design of multimodal connections and the experience of accessibility that results from them. On the supply side, last-mile access involves the coordination of walking, cycling, micromobility, and feeder transit with rail services, supported by digital systems that unify planning, ticketing, and payment. On the demand side, it reflects how efficiently and equitably travelers can reach stations within these coordinated networks. Together, these physical and institutional dimensions extend the functional reach of rail, reduce transfer barriers, and reinforce its role as the backbone of sustainable urban mobility. As cities strive to reduce car dependency while promoting inclusivity and accessibility, last-mile access has become a key indicator of how infrastructure, technology, and governance intersect to deliver more equitable transportation systems. Full article

Anthracnose caused by Colletotrichum graminicola (Ces.) G.W.Wils is a significant disease of maize (Zea mays) worldwide. To obtain an efficient biocontrol strain and elucidate its mechanisms, 103 bacterial isolates were obtained from soil samples collected in the Tianshan Mountains, Xinjiang, China. Among these, Bacillus atrophaeus F4’s fermentation broth had the highest efficacy in controlling maize anthracnose, reaching 79.78%. To further investigate biocontrol mechanisms of F4 strain, its complete genome was sequenced, assembled, and annotated. Lipopeptides extracted from the fermentation broth of F4 were found to strongly inhibit the growth of hyphae and the germination of conidia in the pathogen. Microscopic and biochemical analyses indicated that the lipopeptide extract inhibited chitin synthesis and disrupted the integrity of the cell wall and membrane, thereby exerting antifungal effects. Further MALDI-TOF MS analysis identified antimicrobial compounds, including surfactin, iturin, and fengycin B, in the lipopeptide extract. Furthermore, plate antagonistic test showed that F4 strain exhibited broad-spectrum antagonistic activity against multiple plant pathogenic fungi. F4 strain also displayed motility, biofilm-forming capacity, and the ability to produce extracellular enzymes such as proteases and amylases, which are associated with biocontrol activity. These findings suggest the significant potential of B. atrophaeus F4 as a biocontrol agent against maize anthracnose. Full article

The benefits of work-integrated learning (WIL) in higher education are well documented. Conversely, the assessment thereof, across disciplines, remains challenging. WIL is embedded in the environmental health (EH) degree at a University of Technology (UoT) in South Africa (SA), with similar challenges. The absence of explicit competency indicators and associated assessment criteria in the current curriculum necessitates an understanding of the full range of professional competencies necessary for achieving environmental health goals. Concomitantly, research relating to EH WIL and its assessment in EH programmes are sparse. The aim of this study is to present a holistic assessment framework for competencies developed due to WIL. Using a qualitative design, data collection occurred through in-depth interviews, document analysis and focus group discussions. Data analysis was guided by the Legitimation Code Theory’s (LCT) Specialization dimension. The results foregrounds competencies beyond mere knowledge integration and technical skill development in WIL. It is thus recommended that current assessment be altered to adopt holistic assessment of EH WIL and include the full range of professional competencies essential for EH practice success. Further research may explore where additional opportunities exist for authentic assessment of the foregrounded competencies, throughout the programme, additional to the WIL assessments. Full article

Unanticipated interplay among digital access, institutional prestige, and support systems shapes academic outcomes in higher education. Survey responses from 387 undergraduates in Taiwan and Vietnam—two markets that experienced 80–130 percent growth in mobile broadband penetration between 2015 and 2023—reveal that greater university resource intensity is associated with higher course grades, whereas Reputation Capital and National Context factors unexpectedly correlate with lower performance. Moreover, while individual motivation robustly predicts achievement, a strong future orientation (long-term mindset) is linked to modest declines in grades, perhaps reflecting difficulties in balancing forward-looking goals with the demands of fast-paced, digitally mediated coursework. These counter-intuitive findings underscore the intricate dynamics of student success in technology-saturated learning environments and suggest that effective use of institutional resources and digital platforms requires targeted interventions—such as training in digital self-regulation and curricular designs that mitigate the downsides of prestige and pervasive connectivity—to optimize academic performance. Full article

Over 90% of roads in the United States are surfaced with asphaltic materials that use petroleum-based asphalt binders, a material with high negative environmental impacts and costs. Biopolymers are a sustainable alternative, as they are sourced from renewable materials and offer the potential to reduce carbon footprint. However, their performance and durability in construction applications remain insufficiently understood. This study analyzes the potential of agar, a biopolymer extracted from red seaweed, to serve as a direct and sustainable replacement for asphalt binders. The study characterizes the rheological properties and durability of agar-based binders and the mechanical and microstructural properties of composites. The study found that agar-based binders exhibited resistance to fungal deterioration, adequate stiffness to resist rutting at temperatures up to 80 °C, and potential for energy efficiencies associated with lower mixing and compacting temperatures. Results indicate that agar-based composites illustrate many properties in line with those of traditional engineering materials. Overall, these results suggest that agar-based materials exhibit promising fresh-state and biodeterioration resistance properties to serve as a sustainable alternative to traditional, petroleum-based asphalt binders. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Background: Infectious proctitis remains an underrecognized entity, although sexually transmitted diseases, especially bacterial infections, exhibit a marked increase in their incidence. Methods: Here, we report a case of a 44-year-old man who presented to the emergency department with lower abdominal and rectal pain, tenesmus, fever and night sweats for the past 6 days. Results: The computed tomography initially revealed a high suspicion of metastatic rectal cancer. The endoscopic findings showed a 5 cm rectal mass, suggestive of malignancy. The histologic examination showed, however, no signs of malignancy and lacked the classical features of an inflammatory bowel disease, so an infectious proctitis was further suspected. The patient reported to have had unprotected receptive anal intercourse, was tested positive for Treponema pallidum serology and received three doses of intramuscular benzathine penicillin G. A control rectosigmoidoscopy, imaging at 3 months and histological evaluation after antibiotic treatment showed a complete resolution of inflammation. Conclusions: Syphilitic proctitis may mimic various conditions such as rectal cancer or inflammatory bowel disease and requires a high degree of suspicion. Clinicians need to be aware of infectious proctitis in high-risk populations, while an appropriate thorough medical history may guide the initial diagnostic steps. Full article

In recent decades, Japan has faced rural depopulation due to urban migration, resulting in widespread property abandonment, the “Akiyas”. This paper presents RE-HAK (Refurbish to Host in Akiyas), a blockchain-based framework promoting a circular economy (CE). RE-HAK enables occupants to live rent-free in Akiyas by completing AI-managed refurbishment milestones via smart contracts. Each milestone—waste removal, structural repairs, or energy upgrades—is verified and recorded on the blockchain. Benefits include: (1) rural economic revival through restoration incentives; (2) sustainable CE adoption; (3) preserving property values by halting deterioration; (4) safeguarding cultural heritage via traditional architecture restoration; and (5) transparent management through automated contracts, minimizing disputes. Findings from three case studies demonstrate RE-HAK’s adaptability across skill levels and project scales, though limitations such as rural digital literacy gaps and reliance on government support for scalability are noted. The framework advances Japan’s revitalization goals while offering a replicable model for nations facing depopulation and property abandonment, contingent on addressing technological and policy barriers. Full article

This study investigates the integration of mentorship, self-regulated learning (SRL), and generative artificial intelligence (gen-AI) to support pre-service teachers (PSTs) before and during work-integrated learning (WIL) placements. Utilising the Mentoring and SRL Pyramid Model (MSPM), it examines how mentors’ dual roles as coaches and assessors influence PSTs’ SRL and explores to what extent gen-AI can assist PSTs in meeting the demands of WIL placements. Quantitative and qualitative data from 151 PSTs, including survey, interview, placement scores, and mentor feedback were analysed using statistical correlation analysis and thematic analysis to reveal varied mentorship approaches. Gen-AI tools are highlighted as valuable in enhancing PSTs’ SRL, providing tactical and emotional guidance where traditional mentorship is limited. However, challenges remain in gen-AI’s ability to navigate complex interpersonal dynamics. The study advocates for balanced mentorship training that integrates technical and emotional support, and equitable access to gen-AI tools. These insights are critical for educational institutions aiming to optimise PST experiences and outcomes in WIL through strategic integration of gen-AI and mentorship. Full article

Relevant skills and physiological traits vary between high-intensity functional training (HIFT) workouts, but it is unknown how altering each programming detail affects their importance. To examine the effect of workout duration on relationships to HIFT performance, twelve men and ten women with ≥2 years of HIFT experience (29.3 ± 7.1 years, 171 ± 7 cm, 80.5 ± 15.6 kg) completed a baseline visit to assess body composition and performance in vertical jump, barbell thruster, and 2000 m rowing tests. Participants returned twice to randomly complete the same circuit of rowing, barbell thrusters, and box jumps for “as many repetitions as possible” in 5 or 15 min. Performance was described by expressed kinetics on each exercise, overall and individual exercises, repetition completion rates and volume load completed, and transition times. Spearman correlation analysis revealed several expected relationships (p < 0.05) but also differences between workout durations. Performance measures, especially rowing (time: ρ = −0.89 to −0.94; power: ρ = 0.88 to 0.93), were more applicable to the 5 min workout. Experience and body composition measures (ρ = −0.47 to −0.50) were more often related to the 15 min transition strategy. These data suggest that increasing workout duration alters the importance of relevant predictive traits. Full article

The ability to monitor forest areas after disturbances is key to ensure their regrowth. Problematic situations that are detected can then be addressed with targeted regeneration efforts. However, achieving this with automated photo interpretation is problematic, as training such systems requires large amounts of labeled data. To this effect, we leverage citizen science data (iNaturalist) to alleviate this issue. More precisely, we seek to generate pre-training data from a classifier trained on selected exemplars. This is accomplished by using a moving-window approach on carefully gathered low-altitude images with an Unmanned Aerial Vehicle (UAV), WilDReF-Q (Wild Drone Regrowth Forest—Quebec) dataset, to generate high-quality pseudo-labels. To generate accurate pseudo-labels, the predictions of our classifier for each window are integrated using a majority voting approach. Our results indicate that pre-training a semantic segmentation network on over 140,000 auto-labeled images yields an $F1$ score of $43.74$% over 24 different classes, on a separate ground truth dataset. In comparison, using only labeled images yields a score of $32.45$%, while fine-tuning the pre-trained network only yields marginal improvements ($46.76$%). Importantly, we demonstrate that our approach is able to benefit from more unlabeled images, opening the door for learning at scale. We also optimized the hyperparameters for pseudo-labeling, including the number of predictions assigned to each pixel in the majority voting process. Overall, this demonstrates that an auto-labeling approach can greatly reduce the development cost of plant identification in regeneration regions, based on UAV imagery. Full article

Forest communities around the world have great difficulties in utilizing the economic potential of their forests, especially timber, under current technical requirements and legal frameworks. The present study examines the feasibility of motor–manual timber management among indigenous Chiquitano communities in Bolivia’s Eastern Lowlands. It evaluates local practices, tests technical optimization options, and assesses their technical, financial, and environmental impacts. Findings reveal that traditional motor–manual timber production is scarcely profitable, exacerbated by burdensome legal frameworks and limited market access. However, motor–manual forest management remains an essential source of income for communities, and it constitutes an important option for rural development. Field tests demonstrate that, with the use of better equipment such as quality chainsaws, and improved maintenance and workflows, productivity and profitability of local logging can be enhanced. Despite a low environmental impact, optimized motor–manual timber management continues to be constrained by governance challenges, logistical limitations, and limited markets for locally produced timber. The study recommends optimizing these aspects, including targeted technical support, market development, simplified legal frameworks, and the setting up of robust local governance structures to replace ineffective centralized command and control approaches. These improvements would enable communities to sustainably use timber from their forests while addressing their socio-economic needs. The findings underscore the potential of logging by local communities as an alternative to large-scale mechanized logging, for Bolivia and in other tropical forest countries. Full article