Search Results (259)

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences ($P_{95}$). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article

Artificial Intelligence (AI) has transitioned from a specialized research area to a ubiquitous socio-technical infrastructure influencing sectors from healthcare and law to manufacturing and defense. In tandem with its transformative promise, AI has created an exponentially expanding ethics literature questioning, fairness, transparency, accountability, and justice. This review synthesizes publications and key policy developments between 2019 and 2025, bringing sectoral discourses together with cross-cutting frameworks. Grounded in a systematic scoping review methodology, we frame the field along four meta-dimensions: trust and transparency, bias and fairness, governance & regulation, and justice, while we investigate their expression across diverse sectors. Special attention is dedicated to healthcare (patient trust and algorithmic bias), education (integrity and authorship), media (misinformation), law (accountability), and the industrial sector (data integrity, intellectual property protection, and environmental safety). We ground abstract principles in concrete case studies to illustrate real-world harms and mitigation strategies. Furthermore, we incorporate pluralistic ethics (e.g., Ubuntu, Islamic perspectives), environmental ethics, and emerging challenges posed by Generative AI and neuro-AI interfaces. To bridge theory and practice, we propose an operational governance framework for organizations. We contend that success involves transitioning from principles toward ethics-by-design, pluralistic governance, sustainability, and adaptive oversight. This review is intended for scholars, practitioners, and policymakers who need a comprehensive and actionable framework for navigating the complex landscape of AI ethics. Full article

This study explores the structural safety, mechanical response and optimal construction parameters of the Horizontal Directional Drilling (HDD) technology applied in airport rigid pavements novelly for navigation lighting renovation. This study adopts a combined research method of three-dimensional finite element modeling (FEM) and field tests (full-scale 4C and 4E class airport runway sections). The reliability of the model is verified by the measured data using a Heavy Weight Deflectometer (HWD). The effects of drilling depth, drilling position and typical aircraft loads on the stress and deformation at the bottom of the pavement slab are systematically analyzed. Then, drilling, grouting and non-destructive testing are carried out in the field full-scale test section to investigate the change in pavement bearing capacities. The results show that minimized influence on the mechanical properties of the pavement can be achieved by using 15 cm drilling depths at either slab center or joints. The pavement stiffness slightly decreases by a maximum of 18.9% after drilling. According to the field grouting test, the Impulse Stiffness Modulus (ISM) of most measuring points can be recovered to the original level before drilling. The use of a 10 cm diameter HDD driller meets the structural safety requirements of airport pavements. The HDD technology induces minimized pavement damage and influence on the bearing capacity of the airport runway structure compared with traditional construction technologies, highlighting its advantages in airfield navigation lighting renovations. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

Research on men’s mental health points out gender differences in help-seeking and access to care. Traditional masculine norms (i.e., emotional repression, self-reliance, “being strong”) and gender bias might conceal distress, delay treatment, and help to explain higher burdens of addiction, violence, and suicide alongside lower recorded affective/anxiety diagnoses. An exploratory narrative review was conducted. PubMed, Scopus, and Web of Science were searched for 2015–2025 studies using MeSH and terms on men’s mental health, masculinities, and stigma. Eleven studies identified attitudinal barriers (i.e., self-stigma, shame, symptom minimization, mistrust, etc.) and structural barriers (i.e., limited tailored services, navigation difficulties, costs, bureaucracy, etc.) that delay identification of psychological distress symptoms, weaken therapeutic alliance, and increase dropout, especially when therapy is perceived as impersonal or ineffective. Intersectional factors (i.e., class, age, ethnicity) further contribute to access and they need to be included in the field of men’s mental health. Gender-sensitive approaches and alternative masculinity role models have the potential to enhance engagement and legitimize emotional experience. To sum up, hegemonic masculinity-related gender norms, acquired through gender-differentiated socialization, are associated with adverse mental health outcomes among men. A lack of gender-sensitive awareness campaigns to reduce stigma around men’s mental health may hinder prevention, delaying early identification and timely intervention. Therefore, men’s mental health care should integrate gender and intersectionality transversally to improve prevention, access, diagnosis, treatment, adherence, and outcomes, supported by professional training and tailored therapeutic tools in clinical routine practice. These findings underscore the need to promote healthier, more egalitarian masculinities and to deconstruct stigmas associated with help-seeking and mental health service. Full article

The enhanced long-range navigation (eLoran) system serves as an important backup method for the global navigation satellite system (GNSS) system. In long-distance transmission scenarios, the signal propagation delay of the eLoran system is affected by fluctuations in meteorological factors along the path. Regarding these issues, such as the potential timing system errors caused by meteorological factors and the limitation on the accuracy of the timing system, in this paper, an innovative prediction model is proposed to predict the propagation delay data by fusing the propagation delay data of multiple differential reference stations on the path and the path-weighted meteorological data. By collecting and processing actual data, four types of prediction tasks were designed. Comparative analyses of the prediction performance of eight common models were conducted on a unified dataset. The results show that the Pucheng–Zhengzhou path-weighted ten-factor back-propagation neural network (PZWT-BPNN) model performs the best, achieving a balance between prediction accuracy and training efficiency. This model effectively suppresses the timing errors caused by meteorological fluctuations and improves the prediction accuracy of the propagation delay of the system, providing corresponding technical support for key fields such as low-altitude economy and transportation. Full article

The emergence of artificial intelligence (AI) coaching challenges the professional roles and identities of human coaches, yet empirical research on this transformation remains scarce. This qualitative field study investigates how professional coaches navigate their roles following the organizational adoption of AI coaching. Drawing on the automation-augmentation paradox, occupational role identity, and role ambiguity theories, we analyzed 15 semi-structured interviews with 12 professional coaches in an Asian coaching firm, contextualized by pre- and post-interviews with the company CEO and the AI provider. Findings reveal that top-down AI implementation triggered significant role ambiguity, catalyzing both protective and expansive identity work. Coaches defended their unique human value (e.g., empathy), while simultaneously experimenting with AI, shifting their perception from threat to collaborative tool. This adaptive process enabled the emergence of distinct AI functions and new “blended” human–AI coaching models. Our resulting conceptual framework demonstrates that resolving the automation-augmentation paradox in relational professions is fundamentally an identity-driven process rather than a technical task reallocation. Furthermore, our findings demonstrate that organizationally induced role ambiguity can serve as a catalyst for professional renewal and vocational adaptation, particularly when supported by participatory leadership, thereby advancing theory and contributing new insights to the literature on technological and vocational transformation in organizational contexts. Full article

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations. Full article

This study represents a bibliometric analysis of the global scholarship on institutional rights in education, based on 192 reviewed publications from the Web of Science database, which includes the 2000–2025 period. Research has developed in three different phases: the initial phase (2000–2006) focused on basic topics such as legal regulation, provision of public services, and administrative discretion; the developmental phase (2007–2013) addressed increasing emphasis on representative bureaucracy, availability, and judicial intervention; and the rapid development phase (2014–2025) emphasized digital transformation, transparency, and international cooperation. The keyword analysis reveals a thematic shift from traditional topics such as the “legal system” and “public service” to current issues such as “digital administration,” “social justice,” and “representative bureaucracy.” Research production remains geographically concentrated in North America and Europe, and contributions from Asia, Latin America, and Africa appear. The main institutions include Harvard University, Oxford University, and Leiden University, while influential authors such as Cooper K.W., Schiff D., and Busuioc E.M. have shaped theoretical and empirical advances. Network visualization and historical clustering illustrate the developing thematic structure and interconnection in the field. This analysis provides valuable knowledge for politicians, educators, and researchers who, in the dynamic global context, navigate the penetration of constitutional principles and education management. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a vital means for sea surface wind speed retrieval, yet its application under extreme typhoon conditions remains challenging. Conventional geophysical models (GMFs) saturate in high wind speed regimes (>20 m/s), and deep learning models (e.g., CNNs) are constrained by data sparsity and feature complexity in typhoon environments. To address these issues, we propose a Comparative Learning method of CNN-Transformer with GMF fusion (CLCTG). The CNN branch extracts local coupling patterns, the Transformer branch models global dependencies, and Kullback–Leibler (KL) divergence loss is used for contrastive learning to heighten sensitivity to complex typhoon wind fields. The GMF branch serves as a physical reference/anchor in the low- to moderate-wind-speed range (<20 m/s) to guide the learning of data-driven branches and avoid overfitting by any single data-driven path. The adaptive fusion branch dynamically reweights the three branch outputs, combining local statistical characteristics to improve performance over approximately 0–30 m/s and extending the range of reliable GNSS-R retrieval from about 20 m/s to about 30 m/s; it should be noted that CLCTG exhibits a performance bottleneck in the extreme >30 m/s range. To further improve high-wind-speed predictions, we introduce environmental features based on their correlation with wind speed; ablation experiments demonstrate that the combined use of environmental parameters and CYGNSS features maximizes overall accuracy. Testing on five typhoons from the Eastern and Western Hemispheres confirms CLCTG’s generalization across diverse geographic contexts, and branch-wise comparisons validate its structural advantages. Buoy observations show peripheral errors below 3 m/s and physically consistent wind speed gradients in the core region. These results indicate that multi-source fusion of CYGNSS and environmental data, coupled with contrastive learning and physical reference, offers a reliable and efficient solution for typhoon wind speed retrieval. Full article

Understanding how strain is transferred across the interior of tectonic plates is fundamental to quantifying lithospheric deformation. The Central Anatolia Transition Zone (CATZ), situated between the North and East Anatolian fault systems, provides a unique natural laboratory for investigating how continental deformation evolves from localized faulting to distributed shear. In this study, we integrate InSAR analysis with Global Navigation Satellite System (GNSS) velocity data, and stress tensor inversion with supporting gravity and seismic datasets to characterize the geometry, kinematics, and geodynamic significance of the CATZ. The combined geodetic and geophysical observations reveal that the CATZ is a persistent, left-lateral deformation corridor (i.e., elongated zone of Earth’s crust that accommodates movement where the landmass on the opposite side of a fault system moves to the left relative to an observer) accommodating ~4 mm/yr of shear between the oppositely moving eastern and western sectors of the Anatolian Plate. Spatial coherence among LiCSAR-derived shear patterns, GNSS velocity gradients, and regional stress-field rotations defines the CATZ as a crustal- to lithospheric-scale transition zone linking the strike-slip domains of central Anatolia with the subduction zones of the Hellenic and Cyprus arcs. Stress inversion analyses delineate four subzones with systematic kinematic transitions: compressional regimes in the north, extensional fields in the central domain, and complex compressional–transtensional deformation toward the south. The CATZ coincides with zones of variable Moho depth, crustal thickness, and inferred lithospheric tearing within the retreating African slab, indicating a deep-seated origin. Its S-shaped curvature and long-term evolution since the late Miocene reflect progressive coupling between upper-crustal faulting and deeper lithospheric reorganization. Recognition of the CATZ as a lithospheric-scale transition zone, rather than a discrete active fault, refines the current understanding of Anatolia’s kinematic framework. This study demonstrates the capability of integrated satellite geodesy and stress modeling to resolve diffuse intra-plate deformation, offering a transferable approach for delineating similar transition zones in other continental regions. Full article

Trajectory planning for intelligent connected vehicles (ICVs) must simultaneously address safety, efficiency, and environmental impact to align with sustainable development goals. This paper proposes a novel hierarchical trajectory planning framework, designed for intelligent connected vehicles (ICVs) that integrates a semantic corridor with a spatiotemporal potential field. First, a spatiotemporal safety corridor, enhanced with semantic labels (e.g., low-carbon zones and recommended speeds), delineates the feasible driving region. Subsequently, a multi-objective sampling optimization method generates candidate trajectories that balance safety, comfort and energy consumption. The optimal candidate is refined using a spatiotemporal potential field, which dynamically integrates obstacle predictions and sustainability incentives to achieve smooth and eco-friendly navigation. Comprehensive simulations in typical urban scenarios demonstrate that the proposed method reduces energy consumption by up to 8.43% while maintaining safety and a high level of comfort, compared with benchmark methods. Furthermore, the method’s practical efficacy is validated using real-world vehicle data, showing that the planned trajectories closely align with naturalistic driving behavior and demonstrate safe, smooth, and intelligent behaviors in complex lane-changing scenarios. The validation using 113 real-world truck lane-changing cases demonstrates high consistency with naturalistic driving behavior. These results highlight the framework’s potential to advance sustainable intelligent transportation systems by harmonizing safety, comfort, efficiency, and environmental objectives. Full article