Search Results (20,382)

The acquisition of high-quality three-dimensional (3D) models of cultural relics often relies on expensive scanning equipment or multi-view image capture, which limits large-scale deployment in real-world heritage conservation scenarios. Large-scale water impoundment in the Three Gorges region has resulted in the permanent submergence of numerous cultural relics and archaeological remains. For many of these artifacts, only a single two-dimensional image remains as the sole visual record, posing significant challenges for reconstructing their original three-dimensional geometry and appearance. This limitation renders traditional multi-view reconstruction and physical scanning methods infeasible. To address this challenge, we propose a generative framework for reconstructing high-fidelity 3D digital models of Chinese Three Gorges cultural relics from a single two-dimensional (2D) image. Building upon recent advances in generative 3D representation learning, the proposed method adopts a transformer-based image-to-triplane architecture to infer an implicit 3D representation directly from a single RGB image. A vision transformer encoder is employed to extract global and local visual features, which are subsequently projected into a compact triplane representation through a cross-attention-based decoder. The reconstructed triplane features are further decoded by a neural radiance field (NeRF) to synthesize dense geometry and appearance, enabling accurate mesh extraction and novel-view rendering. To enhance robustness under in-the-wild conditions, the model implicitly estimates camera parameters during inference without relying on explicit calibration information. The proposed method is evaluated on a dataset of Chinese Three Gorges cultural relics, covering diverse artifact categories and visual styles. Experimental results demonstrate that the proposed framework is capable of producing structurally coherent and visually consistent 3D reconstructions from a single image, effectively preserving key morphological characteristics of cultural relics under limited data conditions. Compared with existing single-image and multi-view reconstruction baselines, the proposed framework exhibits better reconstruction accuracy, visual consistency, and generalization capability. This study provides an efficient and scalable solution for the digital reconstruction of cultural relics and offers a practical pathway for large-scale 3D digitization of heritage artifacts from archival images. This work provides a practical solution for the digital reconstruction of submerged heritage artifacts and contributes to the application of generative 3D modeling techniques in cultural heritage preservation and restoration. Full article

Deep-learning models applied to contrast-enhanced mammography (CEM) are known to be highly sensitive to the input image representation. However, preprocessing is often treated as a secondary step and rarely analyzed as an independent design variable. In this work, we present a systematic engineering analysis of a deterministic, label-independent preprocessing pipeline for CEM images. The pipeline integrates intensity normalization, global histogram matching, local contrast enhancement, denoising, and anatomically constrained breast masking. Using a controlled experimental design, identical deep-learning architectures were trained under different input representations to isolate the impact of preprocessing on classification performance and stability. Across convolutional neural network architectures, anatomically constrained preprocessing consistently improves discrimination performance, reduces variability across cross-validation folds, and enhances training stability. Breast mask-based representations demonstrate substantial gains in AUROC and AUPRC compared to raw DICOM inputs. These findings highlight image preprocessing as a first-class engineering component in medical AI pipelines. Breast masking significantly improves robustness and generalization, independently of network architecture complexity. From a clinical perspective, improving model robustness and sensitivity to malignant lesions may contribute to more reliable AI-assisted decision support in contrast-enhanced mammography, particularly in settings characterized by acquisition variability and heterogeneous patient populations. Full article

Graduate employability has become a central concern for higher education institutions as labour markets undergo rapid transformation driven by digitalisation, technological change, and evolving organisational practices. Universities are increasingly expected to equip graduates with a broad range of employability skills and to collaborate with industry to enhance labour market readiness. However, existing research on employability skills development and university-industry collaboration remains fragmented across disciplines, contexts, and stakeholder perspectives. This systematic review synthesises evidence on how universities prepare their graduates for the labour market through employability skills development and university-industry collaboration. Following PRISMA guidelines, 84 journal articles and conference papers published between 2015 and 2025 were identified through a systematic search of the Scopus database and analysed thematically. The findings reveal that graduate employability is conceptualised as a multidimensional and context-dependent construct encompassing discipline-specific, transversal, digital, career management, and professional disposition-related skills. Employability skills development is most strongly supported through pedagogical approaches that emphasise authentic engagement with professional contexts, including work-integrated learning, project- and challenge-based learning, and technology-mediated collaboration. Reported outcomes extend beyond immediate employment metrics to include enhanced confidence, skills acquisition, employability awareness, curriculum relevance, and organisational learning. However, the effectiveness and sustainability of these initiatives are shaped by structural and institutional conditions, including policy frameworks, resourcing, partnership coordination, and equity of access. The review contributes an integrative synthesis that connects employability skills, pedagogical design, and university-industry collaboration, and outlines implications for policy, educational practice, and future research. Full article

Electrical Impedance Tomography (EIT) represents a promising and non-invasive technique for the characterisation of biological tissues, but its diagnostic performance strongly depends on the electrode configuration, system geometry, and electronic acquisition strategies. In this work, a three-dimensional model based on the Finite Element Method (FEM) is developed to investigate the detectability of epithelial neoplasms through optimised electrode excitation schemes. The adjacent and opposite configurations are systematically compared in terms of impedance contrast, spatial sensitivity, and neoplastic inclusion localisation capability. The simulations were implemented using an open-source finite element solver with heterogeneous multilayer tissue models. The results show that the configuration with opposite electrodes significantly improves impedance contrast and sensitivity in three-dimensional models, allowing for better detection of localised conductivity anomalies. The proposed approach contributes to the design of optimised EIT electronic systems for early and non-invasive screening applications of epithelial cancer. Full article

The echelle spectrometer utilizes an echelle grating as the primary dispersive element, combined with a prism or planar grating for cross-dispersion, to form a two-dimensional spectral image on an area-array Charge-Coupled Device (CCD). Compared with traditional spectrometers, this configuration provides superior spectral resolution, broader wavelength coverage, enhanced transient direct-reading capability, and higher energy throughput within a similar footprint. However, the use of area-array detectors significantly increases system cost, limiting adoption in cost-sensitive applications. To reduce cost while maintaining performance, we introduce a digital micromirror device (DMD) as a spatial light modulator to replace the traditional area-array detector, paired with a highly sensitive photomultiplier tube (PMT) for signal acquisition. The designed system operates across a wavelength range of 270 to 800 nm within a compact footprint of approximately 307 mm × 210 mm × 150 mm. The focused spot is accurately positioned on the DMD surface across the entire band, with the root mean square (RMS) spot radius smaller than a single micromirror’s size. Spectral information is efficiently coupled into the PMT via a focusing mirror by selectively flipping the DMD micromirrors for detection. Full article

Background/Objectives: Magnetite nanoparticles represent promising candidates for a broad spectrum of biomedical applications, ranging from in vitro diagnostic assays to in vivo imaging, hyperthermia, and targeted drug and gene delivery, with some nanoagents already approved for clinical use. A critical determinant of their functionality is the nanoparticle coating, which facilitates beneficial interactions within biological systems. In the context of tumor-targeted therapeutic delivery, key design parameters—particularly surface coatings—can be optimized to enhance treatment efficacy by modulating blood circulation kinetics, biodistribution, and other critical properties. However, current preclinical screening methods primarily rely on cell culture models to identify potential nanocarriers, yet these systems often poorly correlate with actual in vivo performance. This discrepancy highlights the necessity of incorporating more biologically relevant testing platforms, such as high-throughput in vivo assays. Methods: In this work, we employed an original magnetic particle quantification (MPQ) technology to systematically evaluate the blood circulation kinetics and biodistribution patterns for magnetite nanoparticles with 17 different coatings across multiple organs and tissues, including the liver, spleen, lungs, kidneys, heart, tumor, brain, peripheral blood, muscle, and bone. This methodology offers high sensitivity, user-friendly operation, and provides quantitative measurements across a broad dynamic range of nanoparticle concentrations. These advantages enabled high-throughput acquisition of precise blood circulation and biodistribution data. In addition, histological analysis was conducted to evaluate nanoparticle penetration depth within tumor tissue. Results: Here we conducted a comprehensive study of the effect of 17 different polymer-, lectin-, and small molecule-based coatings on the behavior of magnetite nanoparticles in vivo. For each type of obtained nanoparticles, we implemented passive targeting as well as magnetic targeting, the latter using an external magnetic field localized in the tumor area. Conclusions: The collected dataset provides critical insights into how surface modifications influence nanoparticle performance in complex biological systems, offering valuable guidance for optimizing therapeutic nanocarrier design. Full article

Precision agriculture employs advanced technologies to enhance farm productivity and sustainability; however, selecting the most appropriate tools can be challenging for small and medium-sized farms. This study conducts a comparative analysis of ten key precision agriculture technologies (PATs): remote sensing, GPS, GIS, VRT, soil & crop sensors, DSS, UAVs/Drones, AI & ML-based precision farming, autonomous agricultural machinery, and IoT-based smart farming. The analysis employs a neutrosophic set-based multi-criteria decision-making (MCDM) framework. Domain experts evaluated ten representative technologies using a structured questionnaire based on ten critical criteria, including spatial-temporal accuracy, data acquisition latency, scalability, robustness, interoperability, environmental resilience, economic feasibility, and agro-ecological impact. A hybrid MCDM methodology was employed, integrating neutrosophic entropy and DEMATEL to construct criterion weights. Furthermore, we utilized neutrosophic DEMATEL to identify inter-criterion causal relationships. Neutrosophic TOPSIS, enhanced by a newly proposed hybrid Cosine-Jaccard similarity measure, was introduced to rank the alternatives under conditions of uncertainty. The findings reveal that IoT-based smart farming solutions achieved the highest overall score, followed by remote sensing and decision-support system (DSS) platforms. At the same time, variable-rate technology and sensor networks received lower rankings. The findings underscore the appropriateness of particular PATs for small and medium-scale farming contexts and illustrate the effectiveness of neutrosophic MCDM in addressing ambiguity and indeterminacy. The comparative insights provide direction for researchers, policymakers, and practitioners in prioritizing precision agriculture technologies and strategies to enhance sustainable practices in small and medium-scale farming. Full article

The overreliance on chemical pesticides in sub-Saharan African (SSA) for agriculture poses major challenges to sustainable agriculture, ecosystem and human health, biodiversity, and environmental sustainability. While genetically modified (GM) crops have demonstrated potential to lower pesticide use and increase crop yield, their widespread adoption remains limited across SSA, with gaps in knowledge on their yield, benefits and policies impacting their uptake. In this study, a literature-based approach was used to synthesize evidence from peer-reviewed articles and government reports published between 2010 and 2025 on pesticide use, farm productivity, and wellbeing of farmers across three focus countries: Nigeria, South Africa, and Burkina Faso. The summary of approved GM crops, events and utilisation across the three focus countries was also retrieved from the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. Cross-country comparisons were conducted to highlight lessons learned from successful and stalled GM crop programs and to identify regulatory, socio-cultural, and economic factors shaping adoption. It is shown that while GM crops can significantly reduce pesticide usage and production costs, challenges such as public hesitancy, regulatory hurdles, limited farmer awareness, and concerns about ecological consequences continue to hinder wider uptake across the continent. Similarly, weak seed systems and the lack of regionally harmonized biosafety regulations also constrain adoption. In areas where GM crops have been successfully adopted, it was demonstrated that supportive policy frameworks, transparent biosafety regulations, effective seed certification and distribution systems, and sustained community engagement increased farmer confidence and accelerated adoption. Hence, for GM crops to be more widely adopted for sustainable crop protection in sub-Saharan Africa, governments and stakeholders must strengthen biosafety systems, invest in farmer education, promote regional regulatory coordination, and facilitate public–private partnerships. Full article

This study presents an integrated workflow for acquiring, processing, and fusing terrestrial laser scanning and Unmanned Aerial Vehicle (UAV) photogrammetric data to generate digital twins of heritage buildings within Heritage Building Information Modeling (HBIM) and Historical Geographic Information System (HGIS) environments. Using a historic wooden church as a case study, the proposed approach demonstrates improved completeness and geometric quality compared to UAV-only models. Dimensional differences between UAV-only and integrated models ranged from 0.8 to 3.2 cm, confirming internal consistency and suitability for documentation purposes. The workflow standardizes key stages of acquisition, scaling, and point cloud fusion, and establishes links between HBIM models at Level of Detail (LOD) 100–300 and conservation requirements. Additionally, it identifies integration points for Artificial Intelligence (AI)-based automation, supporting future developments in classification, segmentation, and conversion of 2D documentation into HBIM. The results highlight the potential of terrestrial laser scanning (TLS)-UAV integration for accurate, replicable heritage documentation and spatial–historical analysis. Full article

SAR systems are widely used in military, scientific, and commercial missions. The use of these systems makes jamming a focal point for users. To reduce the impact of jamming, this phenomenon needs to be better understood. This paper suggests a method for the categorization of jamming mechanisms in synthetic aperture radar (SAR) systems that builds on the physics of SAR acquisitions and jamming signals. It investigates the effects of different types of jammers on SAR images and systematically categorizes them. The categorization is based on the mathematical representation of the physics of the jamming signal and the SAR image formation process. For all suggested categories, jammers are simulated based on a simple, generic scene to investigate their effects on the resulting SAR image. These effects can vary across a wide range—from adding background noise to unfocused responses similar to point target scattering. Moreover, an intelligent jammer is even able to generate false targets virtually or to reduce the signal intensity of real targets by retransmitting signals fully adapted to the image acquisition scenario. Categorizing jammers is a bottom-up task best based on physics. It will be essential in the future when the sky is crowded with constellations of satellites and interference levels increase significantly and become commonplace. Full article

To investigate the relationship between operator fatigue and collision risk under human–machine interaction (HMI) in intelligent tower crane operations, and to reveal the mitigating effects of HMI on fatigue-induced collision risks, a comprehensive data acquisition approach integrating eye-tracking signals, risk indicators, and fatigue scale assessments was proposed and validated through scenario-based experiments. First, two experimental scenarios—traditional mechanical operation and HMI operation—were established. Based on a review of existing studies, representative eye-movement metrics and fatigue scale indicators were selected. Subsequently, operator fatigue states were classified into three levels: low fatigue, moderate fatigue, and high fatigue. A total of 28 participants were recruited to complete fatigue assessments and subsequently perform tower crane lifting tasks under both experimental scenarios. Finally, collision risk under different scenarios was quantitatively evaluated using the safety distance between the crane hook and the rigger, as well as the frequency of collision alarms. The results indicate that, under traditional mechanical operation, increasing fatigue levels were associated with a significant reduction in safety distance between the crane hook and the rigger, accompanied by a marked increase in collision alarm occurrences, resulting in a relatively high overall collision risk. In contrast, under the HMI operation scenario, participants demonstrated superior operational control at equivalent fatigue levels. Specifically, under moderate fatigue, collision risk was reduced from low risk to no risk, while under high fatigue, collision risk decreased from high risk to low risk. These results indicate that, under laboratory-simulated conditions, human–machine interaction can mitigate, to a certain extent, the increasing trend of collision risk when operators perform tower crane lifting operations under fatigue. These findings provide a scientific basis for further optimization of intelligent tower crane operational modes and the development of enhanced safety management strategies. Full article

High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots. Full article

To address the challenges of complex operation, high server deployment costs, and insufficient automated identification capabilities in community-based centralized electrocardiogram (ECG) screening, a novel arrhythmia screening system based on handheld ECG leads and email transmission is proposed. The system is operated in a zero-interaction mode: ECG acquisition is initiated automatically upon skin contact with the electrodes, and upon completion, the ECG signal is automatically analyzed and the email transmission function is triggered—no user intervention being required. First, noise in the ECG signal is effectively suppressed by cascading a zero-phase high-pass filter with a sliding window and a zero-crossing-rate (ZCR) guided adaptive wavelet thresholding technique. Subsequently, RR interval sequences are extracted from the denoised signals and fed into a lightweight bidirectional long short-term memory (BiLSTM) network for automatic arrhythmia detection. In the final step, a 30 s standard ECG, screening status, and acquired image are automatically delivered to clinicians via standard IMAP/SMTP email protocols—eliminating the need for dedicated mobile applications or cloud platforms. Experimental results demonstrated that the relative signal-to-noise ratio (SNR_ECG) was improved by 2.36 dB. On the independent test set, a sensitivity of 97.98%, a specificity of 98.21%, and an AUC of 0.994 were achieved. Furthermore, an end-to-end email transmission latency of less than 7.68 s was recorded. These findings confirm the potential of the proposed system as a low-cost, easily deployable, and elderly-friendly remote ECG solution for primary healthcare settings. Finally, in a pilot screening involving 10 volunteers, one case of arrhythmia was successfully identified, which validated the feasibility of the system. Full article

Different forest ecosystems affect the acquisition and loss of SOC by changing the niche differentiation of above-ground and under-ground, resulting in changes in the utilization efficiency of water and nutrient elements. The impact of different types of forests on carbon storage in forest soils has received significant attention in recent decades, as these ecosystems are critical for mitigating the effects of global climate change. There are significant differences in environmental factors among different types of forests, such as carbon source type, topographic characteristics, soil texture, microbial community status, climate and hydrological conditions. At present, the research on the effects of environmental factors such as climate, hydrological conditions or soil quality on SOC has been well carried out. Nevertheless, the distribution pattern of microbial carbon and particulate organic carbon in subtropical forest ecosystems and their contribution to SOC still need much of scientific research. Forest types have a significant impact on the content and distribution characteristics of MNC and particulate organic carbon fractions, but there is heterogeneity in different forests. Importantly, the random forest analysis showed that MNC and MAOC were the main factors affecting SOC compared with other variables, which indicated MNC and MAOC have higher relative importance to SOC (p < 0.05). Specifically, our research found that the total MNC and BNC content in natural forests and broad-leaved forests were significantly higher than that in coniferous forests (p < 0.05), while the FNC content and FNC/BNC in coniferous forests were significantly higher than that in the other two forests (p < 0.05). In addition, the MAOC content of natural forests was higher than others, which indicated the stability of natural forest is higher than other forests. However, CPOC, FPOC content, and POC/MAOC in coniferous forests were significantly higher than in broad-leaf forests and natural forests. Biotic and abiotic factors profoundly affect the dynamic changes in SOC accumulation and stability. Different environmental factors lead to more MNC and MAOC in forest types with faster decomposition rates. These findings have instructive implications for understanding the contributions of different forest types on SOC stability and accumulation mechanisms in forest soils. Full article

Human information processing is often considered vision-dominant. However, perception is multisensory and shaped by interactions among sensory modalities as well as by top-down processes that integrate prior knowledge and context. Research demonstrates that these mechanisms influence early neural processing and enrich perception beyond purely bottom-up input. For individuals who are blind, this adaptability allows for the effective acquisition of information through alternative sensory channels, provided that accessibility systems are in place. A central challenge is the limited access to written materials, including text, numerical data, and music notation. Assistive technologies such as speech synthesis and Braille have become key solutions. This contribution focuses on Braille, discussing issues of organization, standardization, and technical design. It also introduces the project “Braille Display Screen Based on Long-Wave Infrared Radiation,” which seeks to create a passive Braille display as an alternative to conventional actuator-based devices. Full article