Search Results (15,475)

Operational satellites are critical to modern aerospace infrastructure, supporting essential services such as communication, navigation, and global surveillance. However, the increasing density of satellites and space debris in Earth’s orbit has heightened the risk of collisions, thereby threatening network reliability. This study addresses the dual challenge of managing space debris and enhancing satellite network performance by applying the concept of acyclic matching from graph theory to satellite constellations modeled as honeycomb networks. Acyclic matching identifies edge subsets without shared nodes or cycles, enabling static signal rerouting through pre-computed, loop-free paths. This ensures fault tolerance and efficient resource allocation in increasingly complex satellite constellations. The proposed method derives the general solution for acyclic matching cardinality and determines the maximum matching set for n-dimensional honeycomb networks. This technique aligns with emerging trends in autonomous fault-tolerant systems and adaptive routing protocols, proving particularly relevant for large-scale satellite systems such as Starlink and global navigation constellations. By providing alternative communication paths in the event of satellite or link failures, the approach significantly enhances the scalability, reliability, and resilience of satellite networks, ensuring uninterrupted service and improved space traffic management in the face of rising orbital congestion. Full article

Background: Respiratory syncytial virus (RSV) is a major cause of infant respiratory illness, leading to significant hospitalizations. Two preventive strategies exist: maternal vaccination and a long-acting monoclonal antibody for neonates. In The Netherlands, neonatal immunization is planned to start from autumn 2025 onward, contingent on acceptance by parents and healthcare professionals. Maternal vaccination is already available at own costs. Understanding acceptance, perceptions, and barriers is critical for effective implementation. This study explores these factors to inform strategies for optimal uptake. Methods: This mixed-method study involved semi-structured online interviews with 21 (expectant) mothers (EMs) and 32 healthcare professionals (HCPs) involved in maternal and neonatal care (e.g., pediatricians, youth doctors/nurses, obstetricians, midwives, and general practitioners) and a quantitative descriptive analysis of factors influencing EM choices. Interviews were transcribed and thematically analyzed. Results: Both EMs and HCPs showed strong support for RSV immunization, with a preference for maternal vaccination or a combined approach. Concerns about neonatal injections during the sensitive postpartum period and unfamiliarity with newborn injections (e.g., vitamin K) influenced preferences. EMs noted hesitation about additional pregnancy/postpartum vaccinations, emphasizing the importance of well-timed interventions. HCPs highlighted logistical challenges, such as defining responsibilities, navigating National Immunization Program (NIP) changes, and ensuring readiness. All interviewed individuals value the option to choose between strategies, necessitating informed decision-making and respect for preferences. EMs make their final decision together with their partner, supported by expert information and their personal environment. Conclusions: Support for RSV immunization is high, with maternal vaccination preferred, though neonatal immunization is accepted if appropriately timed. Providing clear personalized and consistent information, heightened public awareness of RSV’s impact, respecting individual choices, and offering options are key to maximizing uptake. Full article

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement. Full article

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

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

Diamorphine (DIM, heroin) is a semi-synthetic opioid that undergoes rapid conversion to 6-monoacetylmorphine and morphine, producing short-lived biomarkers that are difficult to capture during the process. This review critically explores the evolution of analytical techniques for quantitative DIM analysis in biological matrices from 1980 to 2025. It synthesizes findings across blood, plasma, urine, hair, sweat, and postmortem samples, emphasizing matrix-specific challenges and forensic applicability. Unlike previous opioid reviews that primarily focused on metabolites, this work highlights analytical methods capable of successfully detecting diamorphine itself alongside its key metabolites. This review examines 32 studies spanning three decades and compares three core analytical methods: gas chromatography–mass spectrometry (GC–MS), high-performance liquid chromatography (HPLC) with optical detection and liquid chromatography–mass spectrometry (LC–MS). Key performance metrics include sensitivity, sample preparation workflow, hydrolysis control, metabolite coverage, matrix compatibility, automation potential and throughput. GC–MS remains the workhorse for hair and sweat ultra-trace screening after derivatization. HPLC with UV, fluorescence or diode-array detection enables robust quantification of morphine and its glucuronides in pharmacokinetic and clinical settings. LC–MS facilitates the multiplexed analysis of DIM, its ester metabolites and its conjugates in a single, rapid run under gentle conditions to prevent ex vivo degradation. Recent advances such as high-resolution mass spectrometry and microsampling techniques offer new opportunities for sensitive and matrix-adapted analysis. By integrating validation parameters, forensic applicability, and evolving instrumentation, this review provides a practical roadmap for toxicologists and analysts navigating complex biological evidence. Full article

Background/Objectives: Adoption of single-use ureteroscopes (SU) and flexible and navigable suction ureteral access sheaths (FANS) have improved flexible ureteroscopy (fURS) efficiency and safety. However, the impact of scope–sheath pairing is less studied. This study aims to compare four scope–sheath configurations using reusable ureteroscopes (RU) and SU with either 11/13Fr or 12/14Fr FANS. Methods: We retrospectively evaluated 184 patients undergoing fURS for kidney solitary stones of 10–25 mm. Patients were manually matched across four groups: RU-11/13FANS, RU-12/14FANS, SU-11/13FANS, and SU-12/14FANS (46 patients in each). The endpoints were 30-day stone-free rate (SFR), operative time, surgeon-reported visibility (image clarity and procedural continuity) and postoperative complications. Results: Operative time was significantly shorter in single-use scope groups (p < 0.001). Visibility scores were highest in SU-12/14FANS and lowest in RU-11/13FANS across all subdomains. SFR was higher in SU groups. SU-11/13FANS had a significantly higher SFR than RU-12/14FANS (p = 0.027). In the reusable group, the use of 12/14Fr FANS was associated with a lower SFR compared to the 11/13Fr configuration. Complication rates were low (8.2% overall) and comparable among groups. Conclusions: Pairing SU with 12/14Fr FANS provided optimal visibility and good stone clearance without increasing complications. In contrast, RU paired with 12/14Fr FANS showed slightly reduced efficacy, possibly due to impaired maneuverability. The scope–sheath interaction influenced outcomes differently across scope types, underlining the importance of their matching in fURS. Full article

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons. Full article

Collision avoidance technology serves as a critical enabler for autonomous navigation of unmanned surface vehicles (USVs). To address the limitations of incomplete environmental perception and inefficient decision-making for collision avoidance in USVs, this paper proposes an autonomous collision avoidance method based on deep reinforcement learning. To overcome the restricted field of view of USV perception systems, visual assistance from an unmanned aerial vehicle (UAV) is introduced. Perception data acquired by the UAV are utilized to construct a high-dimensional state space that characterizes the distribution and motion trends of obstacles, while a low-dimensional state space is established using the USV’s own state information, together forming a hierarchical state space structure. Furthermore, to enhance navigation efficiency and mitigate the sparse-reward problem, this paper draws on the trajectory evaluation concept of the dynamic window approach (DWA) to design a set of process rewards. These are integrated with COLREGs-compliant rewards, collision penalties, and arrival rewards to construct a multi-dimensional reward function system. To validate the superiority of the proposed method, collision avoidance experiments are conducted across various scenarios. The results demonstrate that the proposed method enables USVs to achieve more efficient autonomous collision avoidance, indicating strong potential for engineering applications. Full article

Introduction: The management of neovascular age-related macular degeneration (nAMD) is constrained by diminishing therapeutic options for retina specialists and their patients when the disease reaches its end stages. Methods: Clinical insights emerge from two case narratives in which patients benefitted from discontinuation of anti-VEGF therapy. Results: Long-term management of nAMD with intravitreal injections of agents targeting vascular endothelial growth factor (VEGF) is crucial for slowing progression of the disease and is generally well-tolerated. However, vision often declines as the disease progresses over time, even with treatment. This article presents strategies for aligning therapeutic goals with their expected visual outcome when an eye has reached end-stage disease. It addresses considerations for how and when to stop treatment when vision becomes limited, taking into consideration the visual status of the fellow eye and incorporating input from low vision specialists who can better assess best-corrected visual acuity (BCVA) and optimize the visual function of patients. We also acknowledge the potential benefits of switching either the dose or the agent that targets VEGF to alter the long-term visual outcome of treatment. Finally, we discuss the importance of taking into consideration related manifestations of the disease, such as macular scarring, geographic atrophy, or other retinal or optic nerve diseases which may limit vision and thus the utility of continued nAMD treatment. Conclusions: Building a strong patient–physician relationship is essential for navigating the shared decision-making process of when to stop treatment for nAMD. Full article

Background: High-performance work systems (HPWSs), while designed to boost corporate performance, can inadvertently create a core organizational paradox, triggering a negative feedback loop. Specifically, their intense focus on performance outcomes can create a climate conducive to unethical pro-organizational behavior (UPB), as employees navigate the pressures and perceived obligations, ultimately undermining the organization’s long-term sustainability and viability. While prior research has identified important singular pathways, the mechanisms through which HPWSs simultaneously generate both perceived obligations and performance pressures remain ambiguous. Methods: Drawing on the Job Demands–Resources (JD-R) model, we propose and test a moderated dual-mediation framework. Using survey data from 473 employees, we examine psychological contract fulfillment and bottom-line mentality as parallel mediators, with moral identity as a moderator, in the HPWS-UPB relationship. Results: The analysis demonstrated that HPWSs influence UPB through two distinct and paradoxical pathways: a pressure-driven path via an increased bottom-line mentality, and a reciprocity-driven path via enhanced psychological contract fulfillment. Moral identity emerged as a crucial, albeit asymmetrical, buffer, with its buffering role being particularly consequential for the pressure-driven pathway, as moral identity also significantly weakened the indirect effect of HPWSs on UPB channeled through bottom-line mentality. Conclusions: These findings offer a holistic, systems-based understanding of the performance-ethics paradox. The validation of a dual-pathway model provides a new blueprint for how a single management system produces contradictory outcomes through competing mechanisms. The identification of key intervention points (e.g., fostering moral identity) offers practical strategies for managers to foster systems that support both high productivity and a sustainable ethical climate. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article

Background/Rationale: Transnational caregiving may be influenced by religious beliefs and cultural traditions that frame elder care as both a moral and religious obligation. While migration alters caregiving dynamics, religious teachings and cultural expectations remain central in guiding transnational caregiving practices. This study examines how Christian Nigerians who have immigrated to Canada navigate caregiving responsibilities within a transnational context, integrating their religion, cultural values, and the practical realities of crossing borders. Methods: This study employed a predominantly qualitative narrative approach, drawing on in-depth interviews with Nigerian Christian immigrants (N = 10) providing transnational care. Data collection involved a pre-interview survey and semi-structured interviews, providing the opportunity for participants to share their lived experiences. Thematic analysis was used to identify recurring themes related to the role of religion and culture in caregiving, ensuring a comprehensive exploration of participants’ perspectives. Findings: Caregiving is shaped by religious duty and cultural obligation, reinforced by biblical teachings and cultural values. Participants view elder care as a moral responsibility, tied to spiritual rewards and familial duty. Despite migration demands, family-based care remains preferred over institutional care, with social stigma attached to neglecting elders. Conclusions: Religion and culture remain integral to transnational caregiving practices, sustaining caregiving responsibilities despite migration-related realities. While religious teachings provide moral guidance and emotional support, cultural expectations reinforce caregiving as a collective and intergenerational duty. Policies and resources are needed that support transnational caregivers, ensuring they can fulfill their caregiving roles while adapting to new sociocultural environments. Policymakers should prioritize the implementation of policies and programs to support transnational caregivers, including family reunification measures, caregiving-related travel provisions, culturally tailored eldercare services, diaspora–local collaborations, organized caregiver support groups, and financial mechanisms such as tax incentives for remittances dedicated to elder care. Full article

This paper presents an optimal control for autonomous vehicles navigating in intersection scenarios. The proposed controller is based on solving a Quadratic Programming optimization technique to provide a feasible control signal respecting actuator constraints. The proposed controller was implemented in a scale-sized vehicle and is executed using only on-board perception and computing systems to retrieve the state dynamics, i.e., an inertial measurement unit and a monocular camera, to compute the estimated states through intelligent computer vision algorithms. The stability of the error signals of the closed-loop system was proved both mathematically and experimentally, using standard performance indices for ten trials. The proposed technique was compared against LQR and MPC strategies, showing 67% greater accuracy than the LQR approach and 53.9% greater accuracy than the MPC technique, while turning during the intersection. Moreover, the proposed QP controller showed significantly greater efficiency by reducing the control effort by 63.3% compared to the LQR, and by a substantial 78.4% compared to the MPC. These successful results proved that the proposed controller is an effective alternative for autonomously navigating within intersection scenarios. Full article

In surgical navigation-assisted pedicle screw fixation, cross-source pre- and intra-operative point clouds registration faces challenges like significant initial pose differences and low overlapping ratio. Classical algorithms based on feature descriptor have high computational complexity and are less robust to noise, leading to a decrease in accuracy and navigation performance. To address these problems, this paper proposes a coarse-to-fine registration framework. In the coarse registration stage, a Point Matching algorithm based on Curvature Feature Learning (CFL-PM) is proposed. Through CFL-PM and Farthest Point Sampling (FPS), the coarse registration of overlapping regions between the two point clouds is achieved. In the fine registration stage, the Iterative Closest Point (ICP) is used for further optimization. The proposed method effectively addresses the challenges of noise, initial pose and low overlapping ratio. In noise-free point cloud registration experiments, the average rotation and translation errors reached 0.34° and 0.27 mm. Under noisy conditions, the average rotation error of the coarse registration is 7.28°, and the average translation error is 9.08 mm. Experiments on pre- and intra-operative point cloud datasets demonstrate the proposed algorithm outperforms the compared algorithms in registration accuracy, speed, and robustness. Therefore, the proposed method can achieve the precise alignment of the surgical navigation-assisted pedicle screw fixation. Full article