Search Results (2,589)

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Background: Hypertriglyceridemia is a lipid disorder characterized by elevated plasma triglyceride levels, and its prevalence increases with aging. This condition contributes substantially to morbidity and mortality in older adults. In settings with limited access to laboratory testing, especially in developing countries such as Brazil, identifying low-cost and easily applicable screening tools is essential. Objective: To investigate the discriminatory capacity of anthropometric indicators of obesity for screening hypertriglyceridemia in older adults. Methods: A population-based cross-sectional study was conducted with 223 community-dwelling older adults (57% women). Independent variables included body mass index (BMI), waist circumference (WC), abdominal circumference (AC), triceps skinfold thickness (TSF), body adiposity index (BAI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and conicity index (CI). Hypertriglyceridemia was defined as triglyceride levels ≥ 150 mg/dL. Discriminatory performance was assessed using receiver operating characteristic (ROC) curves, and associations were examined using Poisson regression with robust variance. Results: The prevalence of hypertriglyceridemia was 35%. Among older men, AC and CI showed the highest sensitivities (88.90% and 77.40%), while WHR and BMI demonstrated the highest specificities (83.10% and 76.90%). In older women, AC and BMI had the highest sensitivities (95.70% and 87.20%), whereas CI and WHtR exhibited the highest specificities (72.50% and 68.80%). All anthropometric indicators were positively associated with hypertriglyceridemia after adjustment for confounders. Conclusions: AC and CI demonstrated the strongest discriminatory capacity for screening older men with a higher probability of presenting hypertriglyceridemia, while AC and BMI showed the greatest discriminatory capacity among older women. In contrast, WHR and BMI had the highest ability to rule out the condition in older men, whereas CI and WHtR performed this role more effectively in older women. These findings show that low-cost anthropometric indicators can be used in a complementary manner, combining the most sensitive and the most specific measures to support an optimized triage process for hypertriglyceridemia in older adults, particularly in resource-limited settings. Full article

This study investigates the causes of traffic accidents involving Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) and their interdependencies. Using a source dataset comprising 3015 ADAS accident records and 1085 ADS accident records from National Highway Traffic Safety Administration (NHTSA), the study categorizes accident severity into four levels and applies association rule mining (ARM) to identify high-frequency risk factor combinations. Key risk factors include environmental, road, vehicle, and accident characteristics. Findings show that ADAS accidents are concentrated in highway straight-driving scenarios, strongly correlated with rainy weather, and often involve rear-end collisions due to delayed driver reactions. ADS accidents predominantly occur in intersection stopping scenarios, favor clear weather, and exhibit better safety performance in non-damage cases with Level 5 (L5) systems, though they still face perception and decision-making challenges in complex scenarios like nighttime wet roads. The study further reveals that vehicle design purpose (ADAS for highways, L5 for urban areas) strongly influences accident severity, with L5 systems reducing fatality risks through advanced perception but still affected by high speeds, extreme lighting, and system aging. Make attributes and technological maturity also significantly impact outcomes. This study provides insights for technological advancement, regulatory improvements, and human–machine collaboration optimization. Full article

Background: Healthcare communication often relies on complex digital infrastructures, yet clinicians increasingly adopt general-purpose Instant Messaging Apps (IMAs) such as WhatsApp^® to meet unmet needs. DonnaRosa, an Italian community of breast cancer specialists founded in 2017, is a Community of Practice (CoP), where experts exchange second opinions, guidelines, and trial opportunities. This paper examines its origins, practices, and implications. Methods: A mixed-methods design was applied: (1) qualitative analysis of chat logs to identify interaction patterns and rules; (2) a 2024 online survey of 54 members (92.5% response rate) exploring demographics, usage, and perceived value; (3) ongoing semi-structured interviews with founders and participants to reconstruct history, recruitment, and professional impact. Results: The group has grown through personal invitations, creating a friendly network of oncologists. Communication is concise, colloquial, and collegial. Activities focus on case discussions, reassurance, interpretation of guidelines, and exchange of research opportunities. This article presents data from an online survey conducted in 2024, showing that the group is widely used for second opinions, often consulted even on weekends and holidays, and perceived as a source of professional support and learning. Members report that participation frequently changes or refines their clinical judgement, especially when guidelines are incomplete or ambiguous. The community also promotes resilience, reduces professional isolation, supports informal collaboration in research projects, and encourages interaction on organisational and healthcare management issues. Conclusions:DonnaRosa illustrates how informal IMAs can evolve into robust infrastructures of care and professional solidarity, complementing formal systems. In the era of artificial intelligence, CoPs like DonnaRosa may become even more relevant: AI tools, especially large language models, can accelerate literature retrieval and data synthesis, while the CoP provides the critical, experience-based interpretation needed for safe and meaningful application. Such a dual infrastructure—technological and human—offers a promising path for oncology, where complexity requires both computational breadth and the depth of expert clinical judgement. Taken together, these findings and the evolving role of AI in clinical communication underscore the need for oncology societies to develop governance frameworks that ensure the safe, accountable, and clinically appropriate use of instant-messaging tools in professional practice. Full article

Traditional geoportals depend primarily on keyword-based search, which often fails to retrieve relevant datasets when metadata are heterogeneous, incomplete, or inconsistent with user terminology. This limitation reduces the efficiency of data discovery and selection, particularly in domains where metadata quality varies widely. This study aims to address this challenge by developing a semantic collaborative filtering recommendation system designed to enhance dataset discovery in geoportals through the analysis of implicit user interactions. The system captures users’ search queries, viewed datasets, downloads, and applied filters to infer feedback and organize it into a user–item matrix. Because interaction data are typically sparse, semantic user clustering is applied to mitigate this limitation by grouping users with semantically related interests through hierarchical relationships represented in the Simple Knowledge Organization System (SKOS). However, as users often need complementary datasets to complete specific tasks, association rule mining is employed to identify co-occurrence patterns in search histories and enhance task-related result diversity. The final recommendation scores are then computed by factorizing the user–item matrix with Alternating Least Squares (ALS), using cosine similarity on the latent user vectors to identify nearest neighbors, and applying a standard user-based neighborhood prediction model to rank unseen datasets. The system is implemented within an existing ontology-based geoportal as a standalone, configurable component, requiring only access to user interaction logs and dataset identifiers. Evaluation using precision, recall, and Precision@5 demonstrates that increasing user interactions improves recommendation performance by strengthening behavioral evidence used for ranking. The findings indicate that integrating semantic relationships and behavioral patterns can strengthen dataset discovery in geoportals and complement conventional metadata-based search mechanisms. Full article

Folk festivals and other intangible cultural heritage have received widespread attention, and their socio-cultural value can be used to promote tourism, strengthen local identity, and build city brands. However, it remains unclear how these intangible cultural heritage festivals transform their multi-dimensional and multi-configuration material characteristics into economic benefits and image enhancement. This study proposes a practical decision-making framework aimed at understanding how different festival design and governance strategies can work synergistically under different cultural conditions. Based primarily on a literature review and expert questionnaire survey, this study identified six stable materialized practice modules: productization, spatialization, experientialization, digitalization, branding/communication, and co-creation governance. At the same time, this framework also incorporates two other conditional intervention properties: classicism and novelty. The interactions between these modules shape people’s understanding of intangible cultural heritage festivals. Subsequently, this study used a multimodal national dataset that included official statistics, industry reports, e-commerce and social media data, questionnaires, and expert ratings to construct module scores and cultural attributes for 167 festival case studies. Through rough set analysis (RSA), this study simplifies the attributes and extracts clear “if-then” rules, establishing a configurational causal relationship between module configuration and classic/novel conditions to form high economic benefits and enhance local image. The findings of this study reveal a robust core built around spatialization, digitalization, and co-creative governance, with brand promotion/communication yielding benefits depending on the specific context. This further confirms that classicism reinforces the legitimacy and effectiveness of rituals/spaces and governance pathways, while novelty amplifies the impact of digitalization and immersive interaction. In summary, this study constructs an integrated and easy-to-understand process that links indicators, weights, and rules, and provides operational support for screening schemes and resource allocation in festival event combinations and venue brand governance. Full article

Objective: This study on children in low-income families explored whether their mental health problems are attributable to distress from five socioeconomic disadvantage factors playing roles in the multiple disadvantage model. These factors are social disorganization, social structural factors, social relationships, health/mental health, and access to care factors. Methods: The present study employed data extracted from the 2021 National Survey of Children’s Health, describing 7540 low-income children. Weighted logistic regression was conducted (with robust standard errors). Results: It showed that such children were more likely to have mental health problems when seven variables were present. The variables were argumentative children, parents’ difficulty with parenting, children’s difficult peer relations, children being bullied, families’ problematic substance use, families’ use of public health insurance, and families’ difficulty accessing mental health services. In turn, children were less likely to have mental health problems in the presence of six variables: a rundown neighborhood, an unsafe neighborhood, children’s Hispanic ethnicity, children’s Asian ethnicity, children’s general good health, and parents’ good mental health. The present study’s findings support the multiple disadvantage model. Conclusions: That is, the five types of factors key to the model (social disorganization, social structural, social relationships, health/mental health, and access to care) were observed to be related to low-income children’s mental health problems. These findings’ three main implications for practice are that it is crucial to (a) ensure children receive mental health services they need; (b) facilitate effective parent–child communication; and (c) provide low-income families with psychoeducation. Their main implications for policy involve two domains. Improving physical environments and safety in poor neighborhoods is necessary, as is enforcing schools’ anti-bullying rules and using schools to foster students’ assertiveness. Full article

Groundwater sustains more than 60% of irrigation in Pakistan’s Indus Basin, yet accelerating depletion, rising salinity and fragmented governance threaten agricultural productivity and rural livelihoods. Although new monitoring technologies and provincial water laws have emerged, a persistent gap remains between scientific evidence, policy frameworks and farmer practices. This study applies the Science–Policy–Practice Interface (SPPI) to examine these disconnects, drawing on qualitative data from multi-stakeholder focus groups and interviews with farmers, scientists and policymakers in Punjab, Sindh and federal agencies. The analysis identifies five governance challenges: weak knowledge integration, fragmented institutions, political resistance to regulation, limited adaptive capacity and under-recognition of farmer-led innovations. While depletion is well documented, it rarely informs enforceable rules and informal practices often outweigh formal regulation. At the same time, farmers contribute adaptive strategies, such as recharge initiatives and water-sharing arrangements, that remain invisible to policy. The findings highlight both the potential and the limits of SPPI. It provides a valuable lens for aligning science, policy and practice but cannot overcome entrenched political economy barriers such as subsidies and elite capture. The study contributes theoretically by extending SPPI to irrigation-dependent aquifers and practically by identifying opportunities for hybrid knowledge systems to support adaptive and equitable groundwater governance in Pakistan and other LMICs. Full article

This paper presents a systematic review of energy management strategies (EMSs) for fuel cell hybrid unmanned aerial vehicles (UAVs). It begins by explaining the necessity of hybrid energy systems. This paper then categorizes existing EMSs into three main classes: rule-based, optimization-based, and learning-based. It provides an in-depth analysis of the core principles, technical advantages, and application challenges for each class. The review also traces the evolution of these strategies from experience-dependent methods to data-driven and autonomous learning approaches. A key finding is that future EMSs will not operate as standalone control modules. By addressing the limitations of current studies, this paper identifies four key development trends: multi-objective collaborative optimization, joint energy-task planning, safe deployment from simulation to real-world environments, and high-fidelity dynamic validation. This work aims to offer theoretical guidance and technological foresight for the research and development of next-generation, high-performance, and high-reliability hydrogen-powered UAVs. Full article

Antisense inhibition of gene expression is usually achieved using nuclease-resistant oligonucleotide analogs that promote mRNA degradation through RNase H or RNase P, or by steric hindrance of translation. Bridge nucleic acids (BNAs) are nucleotide analogs available in a few chemical variants. We evaluated gapmers composed of an oligodeoxynucleotide flanked by BNA residues in a BNA₅-DNA₈-BNA₄ configuration, using the available variants: the original locked nucleic acid (LNA; 2′-O,4′-methylene bridge), cET (2′-O,4′-ethyl bridge), cMOE (2′-O,4′-methoxyethyl bridge), and BNANC (2′-O,4′-aminomethylene bridge). These gapmers were tested in vitro for their ability to induce cleavage of the model aac(6′)-Ib mRNA. All gapmers complementary to a previously identified region suitable for interaction with antisense oligomers induced RNase H-mediated degradation. Instead, only the LNA-containing gapmer also elicited RNase P-dependent cleavage, demonstrating dual RNA- and DNA-mimicking capability. In vitro coupled transcription–translation assays using cell lysates or reconstituted systems confirmed inhibition of expression and ruled out steric hindrance as the mechanism. In contrast, gapmers targeting the ribosome-binding site strongly inhibited expression by steric hindrance. These findings demonstrate that LNA-containing gapmers can exert their effects through multiple mechanisms, depending on the targeted mRNA region, thereby supporting their potential for synergistic inhibition of gene expression. Full article

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder requiring safer and more effective therapeutic alternatives. This study investigates a novel fucoxanthin derivative isolated from the marine brown alga Chnoospora minima using a comprehensive in silico approach. Molecular docking revealed that the derivative exhibited higher binding affinities toward α-amylase (–9.4 kcal/mol) and α-glucosidase (–8.0 kcal/mol) compared to the reference drug acarbose (–8.5 and –7.4 kcal/mol, respectively). Pharmacokinetic analysis predicted good intestinal absorption and P-gp inhibition (0.894) and moderate plasma clearance (7.864 mL/min/kg), while toxicity predictions classified it in toxicity class 3, with no respiratory or ocular toxicity. Drug-likeness evaluation showed only one Lipinski and one Veber rule violation, common for natural products. Molecular dynamics simulations conducted for 100 ns using NAMD 3.0 confirmed stable protein–ligand complexes with average RMSD values of ~1.3 Å and ~1.8 Å for α-amylase and α-glucosidase, respectively, and consistent hydrogen bonding profiles. Structural analysis identified a substitution of the allene bond with an unsaturated ketone at the C8′ position as a key contributor to enhanced enzyme interaction. The findings suggest that this fucoxanthin derivative is a promising natural candidate for T2DM therapy and warrants further investigation through lab experiments (in vitro and in vivo). Full article

Background/Objectives: Pharmacogenetic (PGx) testing is gaining importance in optimizing psychiatric pharmacotherapy, yet routine use remains limited due to cost and unclear patient selection criteria. The CYP Pharmacogenetic Risk Score (CYPRI) is a clinical tool designed to identify psychiatric patients most likely to benefit from PGx testing, based on medication profile, adverse drug reactions (ADRs), and therapeutic drug monitoring (TDM) results. This study aimed to evaluate the clinical relevance of the CYPRI by identifying its weaknesses and gaps in a clinical setting, propose targeted modifications to address those limitations, and assess the applicability of the improved version in a routine clinical setting. Methods: In a retrospective analysis, data from 92 patients with depression at Frankfurt University Hospital were evaluated using the CYPRI score. Its association with the clinical impact of PGx testing, measured by the IMPACT score, was analyzed using ordinal regression and Receiver Operating Characteristic (ROC) analysis. Based on the findings, a revised version of CYPRI was developed and applied to the retrospective cohorts of Frankfurt and Prague. Results: The original CYPRI score was significantly associated with increased IMPACT score, suggesting its clinical value in detecting non-normal CYP2D6 and/or CYP2C19 metabolizers. However, the corrected version (hereafter referred to as CYPRI_cor), which emphasized clinically relevant pharmacokinetic factors, showed improved clinical specificity while maintaining similar discriminative performance. In the Frankfurt cohort, the area under the curve (AUC) for CYPRI_cor was 0.68 (95% CI 0.56–0.79), and in the Prague cohort, the AUC for CYPRI_cor was 0.71 (95% CI 0.60–0.81). While the overall discriminative ability in Frankfurt was slightly lower, CYPRI_cor achieved a specificity of 0.69, enabling more precise identification of patients most likely to benefit from PGx testing. A CYPRI Cut-off of ≥4 was determined to indicate clinical impact. Conclusions: The CYPRI_cor score was designed to optimize and to rule out potential limitations of the original score, particularly regarding the attribution of ADRs and the weighting of TDM results. Although the modifications did not improve discriminative performance in the Frankfurt dataset, the proposed changes remain meaningful. Prospective clinical studies need to verify the clinical utility of the CYPRI_cor. Full article

“GenAI” assistants are gradually being integrated into daily tasks and learning, but their uptake is no less contingent on perceptions of credibility or safety than on their capabilities per se. The current study hypothesizes and tests its proposed two-road construct consisting of two interface-level constructs, namely perceived transparency (PT) and perceived safety/guardrails (PSG), influencing “behavioral intention” (BI) both directly and indirectly, via the two socio-cognitive mediators trust in automation (TR) and psychological reactance (RE). Furthermore, we also provide formulations for the evaluative lenses, namely perceived usefulness (PU) and “perceived risk” (PR). Employing survey data with a sample of 365 responses and partial least squares structural equation modeling (PLS-SEM) with bootstrap techniques in SMART-PLS 4, we discovered that PT is the most influential factor in BI, supported by TR, with some contributions from PSG/PU, but none from PR/RE. Mediation testing revealed significant partial mediations, with PT only exhibiting indirect-only mediated relationships via TR, while the other variables are nonsignificant via reactance-driven paths. To uncover non-linearity and non-compensation, a Stage 2 multilayer perceptron was implemented, confirming the SEM ranking, complimented by an importance of variables and sensitivity analysis. In practical terms, the study’s findings support the primacy of explanatory clarity and the importance of clear rules that are rigorously obligatory, with usefulness subordinated to credibility once the latter is achieved. The integration of SEM and ANN improves explanation and prediction, providing valuable insights for policy, managerial, or educational decision-makers about the implementation of GenAI. Full article

Multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), and degenerative cervical myelopathy (DCM) share features that may confound diagnosis. DCM is caused by degenerative changes in the cervical spine leading to spinal cord compression and injury, resulting in significant disability. Misdiagnosis of DCM for a similar neurological condition can lead to further spinal cord damage from delayed surgical treatment. Here we review the diagnostic criteria, clinical signs and symptoms, and imaging typical for DCM, and two of its clinical mimics, MS and ALS. Shared motor symptoms of all three conditions can make diagnosis difficult, especially early in disease course. Noteworthy differences include neck and shoulder pain in DCM, visual disturbances in MS, and bulbar symptoms and the absence of sensory deficits in ALS. In DCM and MS, MRI is used to support the diagnosis, with specific findings on MRI that differentiate DCM versus MS. In ALS, MRI is used to rule out differential diagnoses. Applying the diagnostic criteria for MS and ALS, as well as understanding the typical presentation and MRI findings of DCM, is crucial. Through discussion of these conditions, this review aims to help limit misdiagnosis rates, allowing for early management, which can improve long-term patient outcomes. Full article

Accurate sea surface temperature (SST) forecasting in coastal upwelling systems requires predictive models capable of representing complex oceanic geometries. This work revisits grid-to-mesh coupling strategies in Graph Neural Networks (GNNs) and analyzes how mesh topology and connectivity influence prediction accuracy and artifact formation. This standard coupling process is a significant source of discretization errors and spurious numerical artifacts that compromise the final forecast’s accuracy. Using daily Copernicus SST and 10 m wind reanalysis data from 2000 to 2020 over the Canary Islands and the Northwest African region, we evaluate four mesh configurations under varying grid-to-mesh connection densities. We analyze two structured meshes and propose two new unstructured meshes for which their nodes are distributed according to the bathymetry of the ocean region. The results show that forecast errors exhibit geometric patterns equivalent to order-k Voronoi tessellations generated by the k-nearest neighbor association rule. Bathymetry-aware meshes with $k=3$ and $k=4$ grid-to-mesh connections significantly reduce polygonal artifacts and improve long-term coherence, achieving up to 30% lower $\mathrm{RMSE}$ relative to structured baselines. These findings reveal that the underlying geometry, rather than node count alone, governs error propagation in autoregressive GNNs. The proposed analysis framework provides a clear understanding of the implications of grid-to-mesh connections and establishes a foundation for artifact-aware, geometry-adaptive learning in operational oceanography. Full article