Search Results (18,085)

Background/Objectives: The segmentation of three-dimensional radiological images constitutes a fundamental task in medical image processing for isolating tumors from complex datasets in computed tomography or magnetic resonance imaging. Precise visualization, volumetry, and treatment monitoring are enabled, which are critical for oncology diagnostics and planning. Volumetric analysis surpasses standard criteria by detecting subtle tumor changes, thereby aiding adaptive therapies. The objective of this study was to develop an enhanced, interactive Graphcut algorithm for 3D DICOM segmentation, specifically designed to improve boundary accuracy and 3D modeling of breast and brain tumors in datasets with heterogeneous tissue intensities. Methods: The standard Graphcut algorithm was augmented with a clustering mechanism (utilizing k = 2–5 clusters) to refine boundary detection in tissues with varying intensities. DICOM datasets were processed into 3D volumes using pixel spacing and slice thickness metadata. User-defined seeds were utilized for tumor and background initialization, constrained by bounding boxes. The method was implemented in Python 3.13 using the PyMaxflow library for graph optimization and pydicom for data transformation. Results: The proposed segmentation method outperformed standard thresholding and region growing techniques, demonstrating reduced noise sensitivity and improved boundary definition. An average Dice Similarity Coefficient (DSC) of 0.92 ± 0.07 was achieved for brain tumors and 0.90 ± 0.05 for breast tumors. These results were found to be comparable to state-of-the-art deep learning benchmarks (typically ranging from 0.84 to 0.95), achieved without the need for extensive pre-training. Boundary edge errors were reduced by a mean of 7.5% through the integration of clustering. Therapeutic changes were quantified accurately (e.g., a reduction from 22,106 mm³ to 14,270 mm³ post-treatment) with an average processing time of 12–15 s per stack. Conclusions: An efficient, precise 3D tumor segmentation tool suitable for diagnostics and planning is presented. This approach is demonstrated to be a robust, data-efficient alternative to deep learning, particularly advantageous in clinical settings where the large annotated datasets required for training neural networks are unavailable. Full article

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

The ecological benefits of urban green spaces depend on their structure and ecological service function. Evaluation systems used to monitor these characteristics show distinct regional variations. This study analyzed China’s urban green spaces, developed a quantitative ecological benefit evaluation system, and comprehensively evaluated the ecological benefits of green spaces in Xi’an city. Suitable evaluation indexes for Xi’an were selected based on field survey data with large-scale samples and high-resolution remote sensing image data. The results showed that the ecological service function of urban green spaces in Xi’an has been substantially improved by ecological planning. Therefore, it is important to evaluate this function as part of the urban planning and design process. Furthermore, increasing the 3D Green Quantity through urban forests can effectively improve the ecological service function. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Arctic regions are warming rapidly, elevating extinction risks and accelerating ecosystem change, yet widely used bioclimatic datasets rarely represent polar-specific ecological constraints. Here we present BioClimPolar_2300 v1.0, a raster bioclimatic dataset designed for terrestrial Arctic biodiversity research under climate change. The dataset includes 33 gridded bioclimatic layers at a 10 km spatial resolution, covering seven discrete temporal intervals from 2010 to 2300 AD. In addition to conventional variables used globally, BioClimPolar_2300 incorporates three polar-relevant constraint domains: (1) polar day–night phenomena (PDNs), including degree-day metrics during polar night and polar day; (2) temperature-defined seasonal cycles (TSCs), including seasonal temperature, precipitation, aridity, and season length; (3) hot/cold stresses (HCSs), capturing indices of extreme summer heat and winter cold. Precipitation during snow-melting days (P_melting) is also included due to its relevance for species depending on subnivean habitats. Climate fields were extracted from CMIP6 models and statistically downscaled to 10 km using a change-factor approach under a polar projection. Monthly fields were linearly interpolated to derive daily grids, enabling the computation of variables that require daily inputs. Validation against observations from 30 Arctic weather stations indicates performance suitable for biodiversity applications, and two exemplar range shift case studies (one animal and one plant) illustrate biological relevance and provide practical guidance for data extraction and use. BioClimPolar_2300 fills a key gap in Arctic bioclimatic resources and supports more realistic biodiversity assessments and conservation planning through 2300. Full article

This study synthesizes empirical evidence on AI-supported skill assessment systems in higher vocational education through a systematic review and meta-analysis. Despite growing interest in generative AI within higher education, empirical research on AI-enabled assessment remains fragmented and methodologically uneven, particularly in vocational contexts. Following PRISMA 2020 guidelines, 27 peer-reviewed empirical studies published between 2010 and 2024 were identified from major international and Chinese databases and included in the analysis. Using a random-effects model, the meta-analysis indicates a moderate positive association between AI-supported assessment systems and skill-related learning outcomes (Hedges’ g = 0.72), alongside substantial heterogeneity across study designs, outcome measures, and implementation contexts. Subgroup analyses suggest variation across regional and institutional settings, which should be interpreted cautiously given small sample sizes and diverse methodological approaches. Based on the synthesized evidence, the study proposes a conceptual AI-supported skill assessment framework that distinguishes empirically grounded components from forward-looking extensions related to generative AI. Rather than offering prescriptive solutions, the framework provides an evidence-informed baseline to support future research, system design, and responsible integration of generative AI in higher education assessment. Overall, the findings highlight both the potential and the current empirical limitations of AI-enabled assessment, underscoring the need for more robust, theory-informed, and transparent studies as generative AI applications continue to evolve. Full article

The deployment of multi-rotor drones in applications such as package delivery and urban air mobility is increasingly prevalent. Aerodynamic interference between rotors in traditional quadrotor drones impairs performance, and vertical offset is a promising solution to mitigate this interference. This study systematically investigates the aerodynamic characteristics of a quadrotor drone with a vertically offset rear-rotor configuration through computational fluid dynamics (CFD) simulations. By varying the vertical spacing ratio between the front and rear rotors (H/R), quantitative analyses were conducted of key performance metrics, including rotor thrust and power loading, with explanations provided from the perspective of the flow field structure. Furthermore, the underlying physical mechanisms influencing the observed performance variations are explored. The results indicate that, under the operating conditions investigated in this study, which include a single rotor RPM, a 10° inflow angle, and a specific forward-flight speed, the vertically offset configuration demonstrates superior aerodynamic performance at H/R = 1. At this spacing ratio, the rear rotor disk avoids most of the downwash-induced velocity generated by the front rotor, allowing partial recovery of the effective angle of attack. Consequently, vortex-propeller interaction (PVI) is significantly weakened, turbulent kinetic energy (TKE) levels in the interference region are reduced, and premature flow separation on the rear rotor blades is suppressed. These combined effects enhance overall aerodynamic efficiency. This study clarifies the role of vertical rotor spacing in regulating aerodynamic interference in multi-rotor drones, offering valuable insights for the aerodynamic design of compact rotorcraft. Full article

Transformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information. However, the rigid patch partitioning in vanilla self-attention destroys spatial structures and consistency in observed scenes, leading to limited CD performance. In this paper, we propose a novel dual-scale pixel aggregation transformer (DSPA-Former) to mitigate this issue. The core of DSPA-Former lies in a dynamic superpixel tokenization strategy and bidirectional dual-scale interaction within the learned feature space, which preserves semantic integrity while capturing long-range dependencies. Specifically, we design a hierarchical decoder that integrates multiscale features through specialized mechanisms for pixel superpixel dialogue, guided feature enhancement, and adaptive multiscale fusion. By modeling the homogeneous properties of spatial information via superpixel segmentation, DSPA-Former effectively maintains structural consistency and sharpens change boundaries. Comprehensive experiments on the LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that DSPA-Former achieves superior performance compared to state-of-the-art methods, particularly in preserving the structural integrity of complex change regions. Full article

Barley is a marginalized crop subjected to several types of abiotic stress but need to intensify for future climate smart crop. This study investigated the drought resistance of barley landraces focusing on agro-morphological and physiological traits under controlled drought conditions. The experiment employed a two-factorial completely randomized design (CRD) with 14 barley landraces (of which 8 completed the maturity period examination) subjected to drought stress at three growth stages (CRI, tillering, and grain filling). Key parameters such as SPAD values (chlorophyll content), tiller number, and yield attributes were measured and analyzed using drought tolerance indices. Fourteen genotypes were initially tested, of which six failed to reach maturity; eight genotypes completed the full growth cycle and were used for yield and stress index analysis. Results revealed significant genotypic variation in drought response. Eight landraces exhibited higher SPAD values under drought, indicating better photosynthetic retention. Notably, AFU202501 demonstrated high yield stability (Stress Tolerance Index, STI = 1.782) under both stress and non-stress conditions, while Saptari Local showed exceptional drought avoidance (low Stress Susceptibility Index, SSI = −0.068) through early maturity and minimal yield reduction. In contrast, genotypes like Muktinath and NGRC 6010 were highly sensitive to drought, with significant yield losses (49–87%). Physiological traits such as chlorophyll retention and phenological plasticity (e.g., accelerated maturity under stress) were critical for drought adaptation. The findings highlight the potential of landraces like AFU202501 and Saptari Local as genetic resources for breeding climate-resilient barley varieties. The study underscores the importance of integrating traditional landraces into modern breeding programs to enhance food security in drought-prone regions. Full article

In 2022, Australia saw an unprecedented outbreak of Japanese encephalitis virus genotype IV (JEV GIV). The outbreak involved 42 human cases with 7 fatalities, as well as affecting >80 pig farms in New South Wales and Queensland. Herein, we designed, constructed, and tested two JEV GIV mRNA vaccines encoding prME, which provided protection against a lethal JEV GIV challenge in an Ifnar-/- mouse model. The vaccines were not codon optimized and included either the Native (full-length) or a Shorter signal peptide, with the latter missing the N-terminal n-region. Two vaccinations with 5 µg of the Shorter vaccine provided neutralizing antibody responses that were significantly lower but overlapped with those seen after vaccination with Imojev, a live attenuated vaccine approved for use in humans. Both mRNA vaccines provided approximately a five to six log reduction in viremia, ≥80% protection against overt disease and weight loss, and mortality. The paper illustrates in-country mRNA vaccine generation in response to a local outbreak, with JEV mRNA vaccines potentially emerging to be easier to manufacture, cheaper, and more suitable for immunocompromised individuals. Full article

With the acceleration of digitalization, smart cities have emerged as a key institutional practice reshaping urban governance and spatial development. However, the impact of smart cities on land use efficiency and the conditions under which these effects are shaped by interactions among different policy tools remain insufficiently understood. This study adopts a policy mix perspective, situating smart city pilots within an institutional environment shaped by regulatory, incentive-based, and enabling policy tools, and systematically examines their impact on land use efficiency and underlying mechanisms. Based on data of 285 Chinese prefecture-level cities over 2000–2021, the study treats smart city pilot as a quasi-natural experiment and applies a staggered difference-in-differences (DID) design, supplemented by moderation and triple-difference models. The results indicate that the smart city pilot significantly enhances land use efficiency overall, although the effects vary across regions and topographical conditions. Further analysis reveals that policy tools with different functional attributes exert differential moderating effects: regulatory policy tools, represented by environmental regulation intensity, negatively moderate the land use efficiency gains of smart cities, while incentive-based tools, such as science and technology fiscal incentives, positively amplify these effects. Additionally, cities implementing both smart city pilots and the “Broadband China” Strategy pilot experience significantly greater improvements, highlighting the enabling policy tools in amplifying smart city performance. Overall, the impact of the smart city pilot on land use efficiency is not isolated but highly contingent on the surrounding policy mix. Interactions among policy tools systematically shape land use outcomes under digital urban governance, offering actionable insights for coordinated policy design. Full article

Water quality observations are vital for effectively managing coastal resources and influencing decisions from emergency beach closures to aquaculture leasing agreements. This study focuses on deriving two water quality parameters—Chlorophyll a (Chl-a) and suspended particulate matter (SPM)—through the high-resolution multispectral imager (MSI) onboard the Sentinel 2A&B satellites, specifically for the Ningde coastal region, which is a crucial aquaculture hub in China. Since more than 90% of the signals captured by satellites are affected by atmospheric interference, it is crucial to apply a process called “atmospheric correction” (AC) to isolate the water contribution, known as water leaving reflectance, from the radiance measured at the top of the atmosphere. Our research assesses five published AC models and various algorithms designed to accurately estimate Chl-a and SPM from water leaving reflectance. We determine the most effective combination by comparing these findings against in situ data gathered from eleven locations in the Ningde coastal region (POLYMER-SOLID with lowest metric RMSLE (0.29), and MAE (1.68) and POLYMER-MDN with the lowest metric RMSLE (0.59), and MAE (0.56)). Our study underscores the importance of selecting locally validated AC models and algorithms for generating water quality products, as this enhances the utility of remote sensing data in monitoring water quality. Moreover, we conduct a spatiotemporal analysis of the water quality parameters from 2016 to 2021, revealing significant interannual variability that underlines the need for continuous monitoring and robust data analysis in coastal management efforts. Full article

Small detector arrays, which are designed to record relatively small EAS with energy in the ‘knee’ region, are often equipped with clocks that measure the time difference between fast signals from several detectors, as well as spectrometric channels that provide the amplitudes of these signals. When analyzing them to determine the angles of arrival and the size of the registered showers, it is important to take into account uncertainties, i.e., the dispersion of measured time differences and shower size relative to the ‘true’ values, which are unknown in the actual situation. Analyses of these spreads are essentially only possible on the basis of correctly performed simulation calculations that take into account all possible stochastic processes in the development of showers in the atmosphere. In this paper, we present a simulation-based analysis using the CORSIKA program of a small EAS array model consisting of four charged particle detectors. We demonstrate the potential offered by ideal timing and how we can infer the energy of the primary particle by analyzing signal amplitudes. The analysis shows that the costs, not only financial, of introducing timing and shower spectrometry are not worth the potential physical gains that we can achieve by using them to analyse small showers. Full article

The Meetings, Incentives, Conventions, and Exhibitions (MICE) sector has become a strategic driver of regional economic development, yet secondary cities often lack the structural, governance, and experiential capacities required for competitive MICE positioning. This study proposes and empirically validates a triangulated analytical framework that integrates structural readiness, stakeholder governance capacity, and tourist perceptions to capture systemic misalignments in emerging MICE destinations, going beyond conventional applied readiness assessments. This study evaluates the preparedness of Nakhon Si Thammarat, Thailand, to develop as a sustainable MICE destination using a triangulated mixed-methods design comprising (1) a city readiness assessment based on TCEB’s eight criteria, (2) a survey of 400 tourists and MICE visitors, and (3) in-depth interviews with 20 key stakeholders. The weighted assessment indicated a moderate overall readiness score (3.48/5), with strengths in environmental management, safety, supporting activities, and accommodation. However, MICE venue capacity and city image remained notably weak. Tourists consistently perceived high readiness across most areas, whereas stakeholders highlighted major systemic issues, including fragmented governance, inconsistent MICE service quality, limited capacity for large events, and inadequate transportation integration. Triangulating these viewpoints reveals three analytically distinct preparation gaps—structural, policy implementation, and experience expectations—demonstrating a fundamental misalignment between experiential appeal and institutional capabilities. This study conceptualizes preparedness as a relational outcome impacted by infrastructure, governance procedures, and market perceptions, adding to the MICE destination and governance literature. The methodology can be used to examine comparable misalignments in other emerging or secondary MICE destinations. The findings guide governance-driven MICE city development plans for sustainability and competitiveness. Full article

In the present study, the effects of electrode geometry and welding current on the tensile-shear strength, failure energy, fracture type, and joint microstructure were investigated during the RSW of ultra-high-strength MS1500 steel to high-strength low-alloy SPFC590 steel, both 1.2 mm in thickness. Three electrode geometries—designated as G0-6 mm, G0-8 mm, and A0—recommended for 1.2 mm sheets according to ISO 5821 were examined. It was found that in the G0-6 mm electrode geometry, which has the smallest contact area, excessive expulsion occurred at lower current levels compared to the other geometries. Consequently, this configuration resulted in lower maximum tensile-shear strength and failure energy values. The highest mechanical performance was achieved with the G0-8 mm electrode geometry, where the tensile-shear strength and failure energy were measured as 19.42 kN and 43.81 J, respectively. For the A0 electrode, although the maximum tensile-shear strength (19.68 kN) was comparable to that of the G0-8 mm geometry, the failure energy was approximately 7% lower (40.94 J). For all electrode geometries corresponding to maximum mechanical strength, a pull-out failure mode was observed, where the nugget region of the SPFC590 steel detached from the base metal and remained adhered to the ultra-high-strength MS1500 sheet. Full article