Search Results (391)

Migraine is a complex neurovascular disorder in which immune signaling intersects with vascular and neural circuits, yet the tissue and cell-type context of common genetic risk remains incompletely defined. We integrated large-scale migraine genome-wide association study (GWAS) summary statistics with Genotype-Tissue Expression (GTEx) v8 expression and splicing quantitative trait loci (eQTLs and sQTLs), Bayesian co-localization, single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from migraine cases and controls, a healthy single-cell multi-omics atlas (assay for transposase-accessible chromatin (ATAC) plus RNA), high-dimensional weighted gene co-expression network analysis (hdWGCNA), and embryo-level spatial transcriptomics. Genetic signals were enriched in peripheral arteries, heart, and blood, and gene-level enrichment highlighted mucosal–smooth muscle organs including the bladder and the cervix endocervix. Cell-type prioritization consistently implicated endothelial and vascular smooth muscle lineages, with additional support for inhibitory interneurons and bladder epithelium. In PBMC T cells, co-expression modules capturing cytotoxic/activation and T-cell receptor signaling programs contained migraine-prioritized genes, including PTK2B, nominating immune activation circuitry as a component of genetic susceptibility. Spatial projection further localized risk concordance to craniofacial/meningeal interfaces and visceral smooth muscle–mucosal structures. Together, these analyses delineate a systemic neuroimmune–vascular architecture for migraine and provide genetically anchored candidate pathways and targets for mechanistic and therapeutic follow-up. Full article

Disentangling the impacts of ecological restoration from climate change is an ongoing challenge in remote sensing since the traditional correlative approaches often cannot elucidate causal mechanisms. To overcome this, we introduce a Causal Remote Sensing Framework that uses multi-source satellite data (2000–2020), machine learning (XGBoost, SHAP) and causal inference (T-Learner) to build pixel-level counterfactuals. Using this framework, we assessed the Return Grazing to Grassland Program (RGGP) on the Qinghai–Tibet Plateau. Our results demonstrate that a warming and wetting climate improved Water yield (WY) while at the same time decreasing sand fixation (SF) in 83.6% of the region. Notably, the restoration project became the main factor that slowed this decline. After controlling for observational selection bias, the program had a net positive effect of (+6.02 t hm⁻²), reducing degradation in 64.6% of treated areas. This framework provides a practical way for the remote sensing community to go beyond change monitoring to allow the diagnosis of the causal mechanisms in complex human-environment systems. Full article

The black soil region of Northeast China (NEC) is China’s most important food production base. Long-term inefficient land use has made its ecosystem vulnerable to widespread degradation, prompting the implementation of ecological restoration projects (ERPs) to enhance ecosystem service (ES) resilience. Yet, the complex interactions among key ESs, including grain production (GP), water yield (WY), soil conservation (SC), and carbon storage (CS), as well as the spatial non-stationarity of their driving factors post-ERPs, have caused spatially heterogeneous, scale-dependent ES relationships. To address these gaps, this study aims to analyze temporal changes in ESs across multiple scales in NEC from 2000 to 2020. By mapping the interactions and quantifying their intensities, we revealed spatial variations in driving factors under different ERPs. The results show that the Natural Wetland Conservation Project (NWCP) and Three-North Shelterbelt Program (TNSP) have led to overall improvements in all ESs. In contrast, the Grain for Green Program (GFGP), the Land Salinity/Sodicity Amelioration Project (LASP), and the Natural Forests Conservation Program (NFCP) are associated with trade-offs between ESs. Interactions between ESs exhibited clear spatial scale dependence, and the dominant drivers varied across scales and restoration contexts. These findings highlight the importance of considering spatial scale and non-stationarity when evaluating ecological restoration outcomes. This study provides a scientific basis for the development and management of ecological restoration programs in intensively managed agricultural regions worldwide, particularly those undergoing multiple, overlapping restoration interventions, from a multi-scale spatial perspective. Full article

The 17th-century stucco altar dedicated to St. Michael the Archangel is an interesting, but very damaged, artwork located in the complex of St. Angel in the little town of Barbarano Romano in Central Italy. During the recent and quite necessary restoration carried out by University of Tuscia students on the Conservation and Restoration of Cultural Heritage Master’s program, some problems with the surface coating were encountered in the cleaning phase. Diagnostic and scientific analyses were crucial to better understanding the composition of these materials to perform the safest and most efficient cleaning procedures. The first of many steps required by this approach was an in situ analysis, starting from on-site analysis and diagnostic documentation through X-ray fluorescence spectroscopy and ultraviolet fluorescence photography, followed by laboratory investigations. The latter included µ-Raman and Fourier transform infrared spectroscopies, gas chromatography coupled with mass spectrometry, and scanning electron microscopy equipped with an energy-dispersive detector. Each technique provided useful data to determine the chemical composition of the white surface coating, which was found to be a non-original overpaint containing lead and organic binder. This overpaint had been applied to retouch the white stucco during a previous restoration project. All this new information contributed to achieving the final decision to remove this layer. Full article

In Building Information Modeling (BIM) of bridge piers, persistent limitations have been observed in the modeling of spiral hoop rebar with variable pitch and diameter. Taking Revit as an example, its built-in family files can only generate spirals with constant geometry. When dealing with non-uniform rebar, designers often have to rely on segmented modeling or manual operations, which is not only time-consuming but also prone to deviations. To solve this problem, this paper proposes a parameterized modeling method based on the secondary development of Revit. By combining the Revit API with the C# programming language, the spiral equation is embedded into the Non-Uniform Rational B-Spline (NURBS) curve reconstruction framework, realizing the continuous modeling of spiral hoop rebar in a unified model. This method also allows users to flexibly input parameters such as cover thickness, rebar diameter, and segment length through a graphical user interface. Through comparative experiments, the proposed method and the traditional family file modeling method were verified respectively in the modeling of a single column and an entire bridge pier. The results indicate that the proposed method reduces the average modeling time of a single bridge pier by 66.5% and that of the entire project by 48.7%. While maintaining high geometric accuracy, this method significantly shortens modeling time and reduces workload, especially demonstrating higher consistency in pitch transition sections and conical sections. Beyond technical performance, this study also demonstrates that the secondary development of Revit provides a practical and feasible solution for the efficient, precise, and generalizable modeling of complex reinforcing bar components in terms of expanding BIM functions, which holds significant practical implications. Full article

Construction projects are inherently exposed to high levels of uncertainty due to technical complexity, multiple stakeholders, and dynamic operating environments. However, empirical evidence on the systematic implementation of risk management practices in developing construction contexts remains limited. Unlike studies that assess the effectiveness or outcomes of risk management, this study addresses the gap by examining perception-based evidence of its implementation at the project and organizational levels in Jordanian construction projects. The study focuses on planning, control and monitoring, perceived advantages, and implementation barriers. A quantitative, survey-based research design was employed using purposive sampling. The statistical population consisted of engineers, project managers, and contractors working in the Jordanian construction sector. Out of 280 distributed questionnaires, 232 valid responses were received (response rate: 82.9%). Data were analyzed using descriptive statistics and one-sample t-tests, with the neutral midpoint of the five-point Likert scale (3.00) used as the reference value. The reliability of the instrument was confirmed by Cronbach’s alpha coefficients ranging from 0.814 to 0.868. The findings indicate generally positive perceptions of risk management implementation, with mean values ranging from 3.84 to 4.13. Risk management planning achieved the highest mean score (4.13), whereas control and monitoring practices were comparatively weaker (3.84). Although 82.3% of respondents reported applying risk management techniques, experience levels remain low to moderate. Key barriers include the lack of structured programs, limited knowledge, and insufficient experience. The results highlight the need for institutionalized risk management frameworks and targeted professional training to enhance systematic implementation. Full article

This article presents a case study from a Social and Health Care School in Denmark, where a rooftop garden was designed to promote student health and support nature-based teaching across subject areas. A novel aspect of the project is the formal integration of the garden into teaching, implying that its long-term impact may extend beyond the students to the end-users they will later encounter in nursing homes and hospitals nationwide. This study applies the Evidence-Based Health Design in Landscape Architecture (EBHDL) process model, encompassing evidence collection, programming, and concept design, with the University of Copenhagen acting in a consultancy role. A co-design process with students and teachers was included as a novel source of case-specific evidence. Methodologically, this is a participatory practice-based case study focusing on the full design and construction processes, combining continuous documentation with reflective analysis of ‘process insights,’ generating lessons learned from the application of the EBHDL process model. This study identifies two categories of lessons learned. First, general insights emerged concerning governance, stakeholder roles, and the critical importance of site selection, procurement, and continuity of design responsibility. Second, specific insights were gained regarding the application of the EBHDL model, including its alignment with Danish and international standardised construction phases. These insights are particularly relevant for project managers in nature-based initiatives. The results also show how the EBHDL model aligns with Danish and international standardised construction phases, offering a bridge between health design methods and established building practice. The case focuses on the EBHDL process rather than verified outcomes and demonstrates how evidence-based and participatory approaches can help structure complex design processes, facilitate stakeholder engagement, and support decision-making in institutional projects. Full article

Global climate change has emerged as a critical challenge for human society in the 21st century. As hubs of population and economic activity, urban and rural areas are increasingly exposed to complex and compounded disaster risks. To systematically evaluate the role of educational intervention in climate adaptability capacity building, this study employs a case study approach, focusing on the “Climate Change Adaptation Education and Teaching Alliance Program” launched in Taiwan in 2014. Through a comprehensive analysis of its institutional structure, curriculum, alliance network, and practical activities, the study explores the effectiveness of educational innovation in cultivating climate resilience talent. The study found that the program, through interdisciplinary collaboration and a practice-oriented teaching model, successfully integrated climate adaptability content into 57 courses, training a total of 2487 students. Project-based learning (PBL) and workshops significantly improved students’ systems thinking and practical abilities, and many of its findings were adopted by local governments. Based on these empirical results, the study proposes that urban and rural planning education should be promoted in the following ways: first, updating teaching materials to reflect regional climate characteristics and local needs; second, enhancing curriculum design by introducing core courses such as climate-resilient planning and promoting interdisciplinary collaboration; third, enriching hands-on learning through real project cases and participatory workshops; and fourth, deepening integration between education and practice by establishing multi-stakeholder partnerships supported by dedicated funding and digital platforms. Through such an innovative educational framework, we can prepare a new generation of professionals capable of supporting global sustainable development in the face of climate change. This study provides a replicable model of practice for education policymakers worldwide, particularly in promoting the integration of climate resilience education in developing countries, which can help accelerate the achievement of UN Sustainable Development Goals (SDG11) and foster interdisciplinary collaboration to address the global climate crisis. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

This study proposes a novel structure-preserving algorithm for the Ginzburg–Landau equation (GLE) by combining the Fourier pseudospectral method with the Exponential Average Vector Field (EAVF) scheme. The proposed numerical framework strictly preserves the energy dissipation property of GLE systems, as validated through theoretical analysis and numerical experiments on solitary wave dynamics. Compared to conventional methods such as the average vector field approach, the EAVF-based scheme demonstrates superior computational efficiency, including faster convergence and enhanced stability under larger time steps, enabling accurate long-term simulation of strongly nonlinear GLE systems. Furthermore, this research incorporates a pedagogical innovation through its implementation within an undergraduate innovation project. By adopting a “problem decomposition–code verification–modular development” training model, students engage in the full cycle of algorithm design, implementation, and validation. This practice-oriented approach significantly enhances students’ competencies in scientific programming, complex problem-solving, and research-oriented thinking, providing an effective paradigm for synergizing advanced computational research with talent cultivation in STEM education. Full article

As architectural educators interested in community engagement and learning about everyday practices in the city, we recognize that teaching community engagement in a practical rather than abstract way is key. This paper presents community-engaged architecture and design pedagogy as potential methods for informing the shift in the role of the architect from top-down to ground-up. This paper presents the author’s pedagogical experimentation based on 25 years of teaching live projects in socially engaged architecture and activism. It describes how a pedagogy combining architecture and activism resulted in the development of an interdisciplinary commons curriculum. The curricula aimed to increase the influence of design practitioners in the development of deliberatively democratic neighborhoods by creating new design practices and outputs. Teaching the political role of the architect from the ground-up rather than from the traditional top-down perspective is challenging, as only a few historical case studies can legitimize and inform its development. This paper describes the content of two pedagogical formats. The ‘Architecture and Activism’ postgraduate architecture and design studio and the following ‘Design for Cultural Commons’ interdisciplinary design postgraduate program. They were both designed to have real-world influence. The ‘Design for Cultural Commons’ postgraduate program enabled the development of a curriculum ranging from modules in social science, art and politics to systems thinking, which is required knowledge for complex neighborhood practices. The city was used as a field of study to discover new knowledge through students’ community engagements. Various theoretical frameworks were employed to develop new forms of emancipatory pedagogy, helping the author unlearn the norms of conventional architectural education. The practice of recalibrating architectural canons and values into a common-based curriculum development is discussed through the framing of learning commons. Full article

We have developed a method for in vivo quantitation of lung delivery of inhaled nebulized drugs by measuring a fluorescent-labeled analog in bronchioalveolar lavage fluid (BALF) collected immediately after inhalation dosing. The effectiveness of delivery of an aerosolized formulation of our proprietary water-miscible vitamin A product to the deep lung (target organ) was studied; the product is being developed for prevention of bronchopulmonary dysplasia (BPD) in preterm infants. The fluorescent retinol analog was incorporated by spiking into a standard formulation, remaining fully compatible with existing nebulizer administration procedures for animal exposure. The method provides quantitation of the delivered dose (DD) to the lung within a few minutes after dosing; fluorescence in BAL in a plate reader allows for simple rapid quantitation of the delivered drug, while avoiding the complexities of other labeling methods (e.g., heavy labels or radioactivity). Data from newborn rat and lamb models showed linear dose responses, validating the method. Approximately 5–10% of the inhaled drug was recovered in BALF in both models, consistent with reports in the literature. The ease of use of the method facilitated various aspects of our project, including the transition to more clinically relevant animal models and aerosol exposure systems. The formulation of this approach could be spiked into other formulations, allowing application of the method to other aerosol drug development programs. Full article

Spatial Daylight Autonomy (SDA) and Annual Sunlight Exposure (ASE) are widely adopted metrics for daylight performance assessment in sustainable building design. While valuable, the complexity of these metrics, particularly due to the influence of indirect bounced light, makes them difficult to interpret, especially in high-density residential buildings with multiple apartment units. Additionally, the computational intensity of such analyses limits their practical use in early-stage design or unit comparison. As a result, potential residents often rely solely on direct sunlight exposure when evaluating units without access to meaningful comparative data. To address this gap, this study proposes a simplified daylight evaluation metric, termed the Annual Daylight Index, that is both intuitive and computationally efficient. The index is defined as the total number of annual sunlight hours received across all floor areas of a building, divided by the number of rooms. Implemented using visual programming within a BIM environment, the method leverages a reverse sunlight tracing approach. Its accuracy and efficiency were verified by comparing results and computation times against established daylight simulation tools. The resulting index enables both micro (unit-level) and macro (building-level) comparisons, offering a practical tool for designers, residents, and researchers engaged in daylight evaluation of multi-unit housing projects. Full article

This research investigates the extent to which behavioral biases—specifically overconfidence and representativeness heuristic—affect linguistic tone, narrative structure, and predictive accuracy of financial reports produced by investment analysts operating across diverse global markets. Drawing upon a comprehensive dataset comprising 1575 equity recommendation reports authored by 15 analysts from four major international investment banks between 2019 and 2022, the study evaluates how cognitive tendencies shape report composition and forecast precision. A mixed-methods approach was employed, incorporating qualitative textual analysis and quantitative modeling through random-effects panel regressions. Key constructs assessed include narrative complexity, optimism, visual content usage, and forecast deviation metrics. Our findings reveal that overconfidence significantly influences the tone and detail of analyst reports but does not demonstrably impact projection accuracy. Conversely, representativeness heuristics were not found to consistently affect either report language or earnings-per-share forecast errors. Institutional affiliation emerged as a significant determinant of predictive success, while demographic factors such as gender, native language, and geographic region had limited explanatory power. These findings imply that investors should treat report tone as an indicator of analyst disposition rather than forecast quality, while financial institutions may benefit from training programs aimed at mitigating narrative and stylistic biases in analyst communication. Full article

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to conflicting objectives. To address the challenge of trade-off between sensing and communication performance, this paper proposes a hierarchical beamforming optimization solution designed to tackle joint sensing–communication problems in such scenarios. The overall optimization problem is decomposed into a two-level “leader-follower” structure. In the leader layer, we introduce a max–min strategy based on the bisection method to transform the non-convex Signal-to-Interference-plus-Noise Ratio (SINR) optimization problem into a second-order cone constraint problem and solve the communication beamforming vector. In the follower layer, the Signal-to-Clutter-plus-Noise Ratio (SCNR) maximization problem is converted into a Semi-Definite Programming (SDP) problem solved via the CVX toolbox. Additionally, we introduce a “spatiotemporal resource isolation” mechanism to project the sensing beam onto the null space of the communication channel. The hierarchical optimization solution jointly optimizes communication SINR and sensing SCNR, enabling an effective balance between sensing accuracy and communication reliability. Simulation results demonstrate the proposed method’s effectiveness in simultaneously improving sensing accuracy and communication reliability. Full article