Search Results (4,595)

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system. Full article

Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN architecture that synergistically combines edge caching, Multi-access Edge Computing (MEC) offloading, and reinforcement learning (Q-learning) for adaptive routing. In the proposed system, popular content is cached at radio access network edges (e.g., base stations) and computation-intensive tasks are offloaded to MEC servers, while a Q-learning agent dynamically routes user requests to the optimal service node (cache, MEC server, or origin) based on the network state. The study presented detailed system design and provided comprehensive simulation-based evaluation. The results demonstrate that the proposed hybrid approach significantly improves cache hit ratios and reduces end-to-end latency compared to traditional CDNs and simpler edge architectures. The Q-learning-enabled routing adapts to changing load and content popularity, converging to efficient policies that outperform static baselines. The proposed hybrid model has been tested against variants lacking MEC, edge caching, or the RL-based controller to isolate each component’s contributions. The paper concludes with a discussion on practical considerations, limitations, and future directions for intelligent CDN networking at the edge. Full article

Teacher preparation programmes must now ensure instructional continuity and quality across face-to-face, online, and hybrid modes, even amid health, climate, or security crises. This mixed-methods study examined which principles policymakers and teacher education directors deem essential for such resilience, and how those principles align with prior research and leadership theory. Semi-structured elite interviews (N = 25) were analyzed inductively to surface field-driven themes and deductively through two models: the ten evidence-based training principles synthesized by Hadad et al. and the six capacities of Striepe and Cunningham’s Crises Leadership Framework (CLF). Results show strong consensus on theory–practice integration, university–school partnerships, and collaborative learning, mapping chiefly to the CLF capacities of adaptive roles and stakeholder collaboration. Directors added practice-oriented priorities—authentic field immersion, formative feedback, and inclusive pedagogy—extending the crisis care and contextual influence dimensions. By contrast, policymakers uniquely stressed policy–academic co-decision-making, reinforcing complex decision-making at the system level. Reflective thinking skills and digital pedagogy, though prominent in the literature, were under-represented, signalling implementation gaps. Overall, the integrated model offers a crisis-ready blueprint for curriculum design, partnership governance, and digital capacity-building that can sustain continuity and quality in pre-service teacher education. Full article

This paper presents the design and implementation of a modular cloud-based architecture that enables generative music capabilities in Digital Audio Workstations through a MIDI microservices backend and a user-friendly VST plugin frontend. The system comprises a generative harmony engine deployed as a standalone service, a microservice layer that orchestrates communication and exposes an API, and a VST plugin that interacts with the backend to retrieve harmonic sequences and MIDI data. Among the microservices is a dedicated component that converts textual chord sequences into MIDI files. The VST plugin allows the user to drag and drop the generated chord progressions directly into a DAW’s MIDI track timeline. This architecture prioritizes modularity, cloud scalability, and seamless integration into existing music production workflows, while abstracting away technical complexity from end users. The proposed system demonstrates how microservice-based design and cross-platform plugin development can be effectively combined to support generative music workflows, offering both researchers and practitioners a replicable and extensible framework. Full article

Rural communities face persistent transport disadvantages due to low population density, limited-service availability, and high operational costs, restricting access to essential services and exacerbating social inequality. Autonomous public transport systems offer a transformative solution by enabling flexible, cost-effective, and inclusive mobility options. This study investigates the operational determinants for autonomous road-based transit systems in rural and peri-urban South-East Queensland (SEQ), employing a structured survey of 273 residents and analytical approaches, including General Additive Model (GAM) and Extreme Gradient Boosting (XGBoost). The findings indicate that small shuttles suit flexible, non-routine trips, with leisure travelers showing the highest importance (Gain = 0.473) and university precincts demonstrating substantial influence (Gain = 0.253), both confirmed as significant predictors by GAM (EDF = 0.964 and EDF = 0.909, respectively). Minibus shuttles enhance first-mile and last-mile connectivity, driven primarily by leisure travelers (Gain = 0.275) and tourists (Gain = 0.199), with shopping trips identified as a significant non-linear predictor by GAM (EDF = 1.819). Standard-sized buses are optimal for high-capacity transport, particularly for school children (Gain = 0.427) and school trips (Gain = 0.148), with GAM confirming their significance (EDF = 1.963 and EDF = 0.834, respectively), demonstrating strong predictive accuracy. Hybrid models integrating autonomous and conventional buses are preferred over complete replacement, with autonomous taxis raising equity concerns for low-income individuals (Gain = 0.047, indicating limited positive influence). Integration with Mobility-as-a-Service platforms demonstrates strong, particularly for special events (Gain = 0.290) and leisure travelers (Gain = 0.252). These insights guide policymakers in designing autonomous road-based transit systems to improve rural connectivity and quality of life. Full article

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS-2014), we exploit the OCP’s formal rollout at the end of 1979—operationalized with a 1980 cutoff—as a quasi-natural experiment. A Fuzzy Regression Discontinuity (FRD) design identifies the Local Average Treatment Effect of being an only-child parent on late-life well-being, mitigating endogeneity from selection and omitted variables. Theoretically, we integrate three lenses—policy durability and lock-in, intergenerational support, and life course dynamics—to construct a cross-level transmission framework: macro-institutional environments shape substitution capacity and constraint sets; meso-level family restructuring reconfigures support network topology and intergenerational resource flows; micro-level life-course processes accumulate policy-induced adaptations through education, savings, occupation, and residence choices, with effects materializing in old age. Empirically, we find that the OCP significantly reduces subjective well-being among the first generation of affected parents decades later (2SLS estimate ≈ −0.23 on a 1–5 scale). The effects are heterogeneous: rural residents experience large negative impacts, urban effects are muted; men are more adversely affected than women; and individuals without spouses exhibit greater declines than those with spouses. Design validity is supported by a discontinuous shift in fertility at the threshold, smooth density and covariate balance around the cutoff, bandwidth insensitivity, “donut” RD robustness, and a placebo test among ethnic minorities exempt from strict enforcement. These results demonstrate how demographic policies generate lasting impacts on elderly well-being through transforming intergenerational support systems. Policy implications include strengthening rural pension and healthcare systems, expanding community-based eldercare services for spouseless elderly, and developing complementary support programs. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, and explanatory queries derived from a real offshore oilfield dataset. Results show that the dual-layer graph consistently outperforms a single-layer baseline. Answer accuracy improves from 0.65 to 0.70, faithfulness from 0.54 to 0.61, and context relevance from 0.69 to 0.72, confirming that the system retrieves factual parameters more reliably and provides conceptually grounded explanations. Gains in evidence recall and coverage are more modest, highlighting areas for further optimization. A case study illustrates the framework’s ability to expand petroleum terminology (e.g., “sandstone → clastic rock”), producing responses that are not only quantitatively more reliable but also qualitatively more informative. The dual-layer design effectively addresses the semantic consistency gap in petroleum QA, offering practical value for reservoir evaluation, lithology interpretation, and technical decision support. These findings demonstrate the potential of GraphRAG to enhance knowledge management and intelligent services in petroleum engineering. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

Object detection is a critical technology for smart city development. As request volumes surge, inference is increasingly offloaded from centralized clouds to user-proximal edge sites to reduce latency and backhaul traffic. However, heterogeneous workloads, fluctuating bandwidth, and dynamic device capabilities make offloading and scheduling difficult to optimize in edge environments. Deep reinforcement learning (DRL) has proved effective for this problem, but in practice, it relies on manually engineered reward functions that must be redesigned whenever service objectives change. To address this limitation, we introduce an LLM-driven framework that retargets DRL policies for edge object detection directly through natural language instructions. By leveraging understanding of the text and encoding capabilities of large language models (LLMs), our system (i) interprets the current optimization objective; (ii) generates an executable, environment-compatible reward function code; and (iii) iteratively refines the reward via closed-loop simulation feedback. In simulations for a real-world dataset, policies trained with LLM-generated rewards adapt from prompts alone and outperform counterparts trained with expert-designed rewards, while eliminating manual reward engineering. Full article

Digital financial services have transformed consumer financial behavior, yet their effects on sustainable development outcomes remain poorly understood. This study examines how mobile financial services (MFS) usage influences financial behaviors across temporal dimensions and investigates the moderating role of financial literacy from a systemic sustainability perspective. Drawing on Construal Level Theory, Dual Process Theory, and Social Cognitive Theory, we analyze data from 21,757 U.S. adults from the 2021 National Financial Capability Study to explore relationships between MFS usage, financial literacy dimensions—objective knowledge (OK), subjective knowledge (SK), and perceived ability (PA)—and both short-term and long-term financial behaviors. The results reveal a dual temporal pattern: MFS usage negatively affects short-term behaviors, including spending control and emergency preparedness, while positively influencing long-term behaviors such as retirement planning and investment participation. Financial literacy dimensions demonstrate differential moderating effects, with OK providing protective benefits against short-term risks, while PA can paradoxically exacerbate these adverse short-term effects. These findings highlight complex implications for sustainable development, demonstrating how individual behaviors aggregate to influence systemic financial resilience and progress toward Sustainable Development Goals related to poverty reduction, economic growth, and inequality reduction. Policymakers should adopt behaviorally informed regulatory approaches that address temporal trade-offs. Educators should design digital-specific literacy programs emphasizing realistic risk assessment alongside confidence-building, thereby promoting sustainable financial behaviors in increasingly digital environments. Full article

Intelligent reflecting surface-assisted unmanned aerial vehicles (IRS-UAVs) have been widely applied in various communication scenarios. This paper addressed the uplink communication problem in wireless sensor networks (WSNs) by proposing a novel double IRS-UAVs assisted framework to improve the pairwise sum rate. Specifically, nodes with relatively short signal transmission distances upload signals via a single-reflection link, while nodes with relatively long distances upload signals through a dual-reflection link involving two IRSs. Within each work cycle, the IRS-UAVs followed a fixed service sequence to cyclically assist all sensor node pairs. We designed a joint optimization algorithm that simultaneously optimized the UAV trajectories and IRS phase shifts to maximize the pairwise sum rate while guaranteeing each node’s transmission rate meets a minimum quality of service (QoS) constraint. Specifically, we introduce slack variables to linearize the inherently nonlinear constraints arising from interdependent variables, thereby transforming each subproblem into a more manageable form. These subproblems are then solved iteratively within a coordinated optimization framework: in each iteration, one subproblem is optimized while keeping variables of others fixed, and the solutions are alternately updated to refine the overall performance. The numerical results show that this algorithm can effectively optimize the flight trajectory of the unmanned aircraft and significantly improve the pairwise total rate of the system. Compared with the two traditional schemes, the average optimization rates are 11.91% and 16.36%. Full article

Background: The digitalization of medical services is promoted as a solution for improving access, quality, and efficiency within healthcare systems. In this context, the study investigates the extent to which digitalization in Romania meets the current needs of patients through a convergent analysis of user perceptions and managerial perspectives. Based on the specialized literature, the research tests two hypotheses: (H1) the implementation of digital technologies significantly contributes to improving the quality of medical services and operational efficiency; (H2) digitalization has a positive impact on patient satisfaction by facilitating access to care and improving communication with medical personnel. Methods: The study adopted methodology is cross-sectional and mixed, including an online mixed-methods questionnaire for patients, distributed between 6 and 14 May 2025, and a qualitative questionnaire with open-ended questions distributed via e-mail to managers from public hospitals through The Administration of Hospitals and Medical Services of Bucharest, between 3 and 24 March 2025. Results: In total, 125 patients and 15 hospital managers participated in the study. Statistical analysis (χ², ordinal regression) and data triangulation highlight a predominantly positive, yet heterogeneous, patient perception of digitalization, with Hypothesis H1 only partially supported (weak, inconsistent, and in some cases negative associations between technology use and perceived service quality). By contrast, H2 was robustly validated, with patient satisfaction strongly linked to tangible benefits, particularly easier access and online appointment scheduling. However, use remains limited to administrative functions, while advanced technologies such as telemedicine or electronic health records are poorly adopted. From an institutional perspective, hospitals predominantly use IT systems for internal purposes, without real patient access to their own data, no interoperability between medical units, and marginal implementation of telemedicine. This reveals a significant gap between user perception and organizational realities, emphasizing the lack of a patient-oriented digital infrastructure. Conclusions: The results highlight the potential of digitalization to enhance patient experience and service efficiency, while also pointing out structural limitations that hinder the full realization of this potential. Patient satisfaction is strongly associated with tangible benefits, particularly easier access and online scheduling, whereas the effect on perceived quality is weaker and sometimes inconsistent. There are significant disparities in digitalization levels between healthcare providers, perceived by patients as public–private differences, and gaps among public hospitals are also confirmed by managerial data. These findings suggest that a successful digital transformation of the medical system in Romania must address both technological infrastructure gaps and organizational barriers, within a coordinated national strategy that ensures interoperability, patient-centered design, and sustainable implementation. Full article

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article

Operating power systems has become challenging due to the complexity of these systems. Stability studies are essential to ensure that a system operates under suitable conditions. Low-frequency oscillation modes (LFOMs) are one of the main branches of system angular stability studies and are often studied in small-signal stability. Many LFOMs in the system may have low and insufficient damping rates, negatively affecting the operation of power systems. Different control strategies have been proposed, such as the Wide-Area Damping Controller (WADC), to adequately and easily dampen these LFOMs. The operating principle of a WADC requires the reception of remote and synchronized signals from system PMUs through communication channels. However, WADCs are subject to communication failures and cyberattacks that compromise their proper operation. This paper proposes a multi-objective optimization model whose variables are the WADC parameters and the objective function guarantees the previously desired and high damping rates for the system under normal conditions and when there are permanent communication failures caused by a Denial-of-Service attack. The design method uses Linear Quadratic Regulator theory, where the parameters of this method are tuned by a bio-inspired algorithm. The studies were performed in the IEEE 68-bus system, considering a set of different operating points. The results achieved in the modal and time domain analysis confirm the successful and robust design of the WADC. Full article