Search Results (2,160)

Polarized side-scattering techniques are widely used in aerosol detection, oceanographic optics, and biomedical sensing due to their high sensitivity to weak optical signals in low-scattering coefficient media. Conventional polarized Monte Carlo methods face significant challenges in such regimes due to geometric mismatch, where photon exit positions deviate substantially from the detector plane. This study addresses the geometric mismatch issue in polarized Monte Carlo simulations for side scattering in low-scattering media (scattering coefficient ${\mu }_s=$ 1 cm⁻¹), where photon exit positions often deviate from the detector plane. We propose a novel algorithm incorporating backward ray tracing with geometric projection correction to enhance simulation accuracy. Experimental validation was conducted using 532 nm laser illumination on both 500 nm polystyrene microspheres (${\mu }_s=$ 0.21 cm⁻¹) and 5 nm TiO₂ nanoparticles (${\mu }_s=$ 1.06 × 10⁻⁶–1.06 × 10⁻⁵ cm⁻¹). The results demonstrate excellent agreement between simulations and experiments, confirming the algorithm’s capability to accurately capture the polarization characteristics of side-scattered light. This work provides a high-fidelity simulation tool for designing optical sensors in low-scattering media and holds direct applicability in nanoparticle concentration sensing and aerosol monitoring. Full article

This study focuses on the performance evaluation of publicly funded agricultural research projects in a structured tabular-data setting characterized by small sample size and heterogeneous features. We construct a project-level performance evaluation dataset covering 24 provincial agricultural research institutions in China, with $n=280$ samples. The target variable is the project self-evaluation score, reflecting overall annual target completion rather than a fixed explicit transformation of the input indicators. To address the limitations of manual evaluation—including subjectivity, poor inter-rater consistency, and potential bias—we propose Light-TabNet, which enhances the model’s fitting capability in small-sample scenarios while preserving interpretability. Interpretability is achieved through sparse decision masks and aggregated feature-attribution analysis, with partial cross-model support from comparison with XGBoost-SHAP rankings. Compared with 13 deep learning and traditional machine learning baselines, Light-TabNet achieves improved accuracy in terms of mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination ($R^2$) (MAE 4.9765, RMSE 8.8140, $R^2$ 0.8891). In a preliminary real-world validation on eight projects from a provincial agricultural research institution, the model’s predicted scores were overall close to ratings provided by a third-party organization, suggesting preliminary practical usefulness in a similar management setting. The results suggest that Light-TabNet can serve as a decision-support tool for the performance evaluation of publicly funded agricultural research projects by providing an objective, traceable, and interpretable quantitative reference. Full article

This study offers a comparative analysis of the symbolism of the soul’s ascent in Gregory of Nyssa’s De vita Moysis and Giovanni Pico della Mirandola’s Oratio. Rather than attempting to establish a linear or exclusive dependence, it focuses on a series of Mosaic themes that articulate a dynamic conception of perfection in both authors. Beginning with Moses as a paradigm of virtuous life, the paper examines the shared anthropology of desire underlying Nyssen’s notion of unending progress and Pico’s sacra ambitio. It then traces the ordered sequence of symbols as it develops in Gregory’s treatise: light and darkness, the mountain of the knowledge of God, Jacob’s ladder, the tabernacle, the eagle, death as consummation, and divine friendship. Through the interplay of these symbols both thinkers configure spiritual growth as an ever-deepening participation in divine unity and truth. Particular attention is given to integration of the classical disciplines of the ancient philosophical curriculum within the Mosaic itinerary, as well as to the conception of truth as gradually apprehensible but ultimately inexhaustible. The paper concludes by pondering the results of the comparative study and reflecting on Pico’s way of assimilating the wide variety of sources in his project of philosophical concord. Full article

Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1–0.2 mm. The OC’s non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications. Full article

Cutaneous squamous cell carcinoma (cSCC) is the second most prevalent skin cancer diagnosed worldwide after basal cell carcinoma. CSCC represents a growing global public health challenge due to its higher potential of local invasion, recurrence, and metastasis. Incidence rates of cSCC are projected to increase due to rising exposures to risks factors. Ultraviolet light exposure is the primary cause, and lighter skin pigmentation, immunosuppressive conditions and skin phototype are the primary risk factors. CSCC typically presents as a red, scaly, flat lesion (in situ tumors) or a red, firm, raised lesion with scale or erosion (invasive tumors). Surgical excision remains the standard-of-care for localized cSCC and is often curative. Although, most patients achieve favorable outcomes, a subset of cSCC exhibits a highly aggressive and metastatic phenotype (postoperative recurrence rates are approximately 5%). Addressing the clinical challenge posed by these high-risk cases requires a more comprehensive understanding of the underlying molecular drivers. This review examines the interaction between transglutaminase 2 (TG2) and the G-protein-coupled receptor 56 (GPR56) as a pivotal driver of the aggressive cSCC phenotype. This molecular axis is particularly significant for its role in the maintenance of epidermal cancer stem (ECS) cells, which contribute to tumor progression and therapy resistance. While the definitive link between the TG2-GPR56 complex and systemic metastasis in cSCC is currently being elucidated, significant evidence from analogous malignancies and in vitro keratinocyte models provides a clear mechanistic roadmap for its involvement in tumor invasion. Full article

This study investigated sulphide textures and trace element chemistry from the Tomingley Gold Project (TGP) region of Central-West NSW, eastern Australia, using in situ techniques. In particular, the study focused on pyrite and arsenopyrite to gain insights into ore-forming processes and determine which trace elements within these minerals can be used as potential pathfinder elements for mineral exploration in the TGP. A total of 41 drill core samples from a variety of lithologies (volcaniclastic, monzodiorite, graphitic siltstone, dacite, andesite) were described and analysed using reflected light microscopy, high-resolution microscopy (via Scanning Electron Microscope or SEM), elemental mapping (via Electron Probe Micro Analysis or EPMA) and targeted trace element analysis of sulphide grains (via Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry or LA-ICP-MS). Findings show that pyrite and arsenopyrite are the major sulphides that host fracture-fill/inclusions of native gold and ‘invisible gold’. Pyrite rich in groundmass inclusions should be evaluated due to their characteristic high concentrations of both As and Au. Pyrite trace element chemistry (Sn, Bi, W, Sb, Au and Se) was able to delineate mineralised from unmineralised samples in volcaniclastics, graphitic siltstones and andesites but was much more challenging for lithologies like dacites and monzodiorites. The study also found that Au may have been introduced into the system earlier and existed as ‘invisible gold’ in earlier generations of pyrite. This study highlighted the utility of in situ techniques to discriminate mineralised signatures from unmineralised samples, and this has proven to be far more effective compared to whole-rock techniques, emphasising the benefits of such datasets in mineral exploration. Full article

Background/Objectives: Research examining the emotional and psychological challenges experienced by couples undergoing in vitro fertilisation (IVF) remains limited. Existing evidence suggests that women undergoing IVF often report elevated levels of depression, anxiety, and emotional distress, whereas men may experience feelings of anger, inadequacy, and self-doubt, especially following unsuccessful treatment cycles. Successful IVF outcomes are commonly associated with intense joy, relief, and fulfilment as couples realise their aspiration to become parents. In light of the limited qualitative research conducted in Greece to date, in the present study, we aimed to explore the lived experiences of couples undergoing IVF treatment, with particular attention to emotional, relational, and systemic dimensions. Methods: A qualitative research design was employed. Semi-structured, in-depth interviews were conducted with six heterosexual couples (aged 18–49 years) residing in Athens and Karditsa, Greece, all of whom had undergone IVF treatment. Interviews were audio-recorded, transcribed verbatim, and analysed using Interpretative Phenomenological Analysis. Results: Our analysis revealed five interrelated superordinate themes with associated subordinate themes: (1) making sense of infertility and IVF, (2) negotiating relationships under the strain of IVF, (3) IVF as an emotionally demanding journey, (4) navigating institutional and systemic barriers, and (5) projecting the future through IVF experience. Lived experiences of infertile couples undergoing IVF treatment highlighted a range of emotions, social pressure, and attitudes towards IVF and related policies. Conclusions: In Greece, where demographic decline has been widely discussed in policy debates, IVF has gained societal and policy attention. For many participants, IVF represented a hopeful pathway towards achieving parenthood despite the emotional and practical challenges involved. Full article

The study sheds light on the subtle interactions among financial development, foreign direct investment (FDI), and the quality of regulatory frameworks, with particular reference to their deep influence on renewable energy use and carbon emissions across 22 developed countries from 2002–2021. The results show an interesting tendency: Financial development and FDI will reduce reliance on renewable energy, whereas a significant increase in GDP per capita will increase reliance. Secondly, carbon emissions have a negative association with the adoption of renewable energy and financial development, though both reduce environmental quality; there is a positive relation between real gross domestic product (GDP) and energy depletion in terms of these toxic emissions. The significant role of regulatory quality as a moderator in this process is particularly striking. There is a direct correlation between financial stability and more robust regulation, resulting in reduced financial liquidity available for investing in renewable projects and restricting the free flow of clean FDI. Crucially, the paper argues that when combined with strong regulation, FDI is more likely to contribute to reductions in emissions, while FYGD, nevertheless regulated at a high level of quality, should raise emissions. Winding up, the result indicates that neither financial depth nor institutional quality, in isolation, is sufficient to deliver significant environmental improvement. Thus, it is urgent to adopt sound green finance policies and to formulate focused regulatory systems that integrate financial development and foreign direct investment with a broader sustainability agenda. Full article

Interlayer-sliding ferroelectricity in van der Waals bilayers enables ultralow-power switching, but practical devices are often limited by contact/interface scattering and weak coupling between polarization and transport. We propose homophase lateral architectures based on bilayer Janus MoSSe: a 1T/2H/1T ferroelectric tunnel homojunction and an H-phase lateral p–i–n photodetector (artificially doped electrode). Metallic 1T electrodes largely eliminate contact barriers and maximize polarization-driven tunneling modulation. Using non-equilibrium Green’s function–density functional theory (Perdew–Burke–Ernzerhof approximation, without explicit spin–orbit coupling), we find that AB to BA sliding reduces the current from the nA range to the pA range, with the minimum current of|I_OFF|_min = 2.83 pA, yielding giant tunneling electroresistance up to 5.3 × 10⁴%. Projected local density of states reveals a non-rigid long-range potential redistribution that reshapes the tunneling barrier and opens high-transmission channels. In the p–i–n photodetector, the response is strongly anisotropic and stacking-dependent: AB reaches photocurrent density J_ph ≈ 7.2 µA·mm⁻² at 2.6 eV for in-plane light versus ≈ 2.9 µA·mm⁻² at 3.5 eV for out-of-plane, and exceeds BA by 1.5–1.8 times due to density of states advantages and Mo-d orbital selection rules. Bilayer Janus MoSSe therefore provides a reconfigurable platform for high-contrast memory and polarization-sensitive photodetection. Full article

This study proposes an explainable machine learning framework for estimating the total project cost (TPC) of AI training-data construction, where cost information is difficult to structure due to heterogeneous workflows and quality requirements. Using 386 public AI training-data projects conducted between 2020 and 2022, we derive 24 numerical predictors from standardized final reports and construct three input tracks: a baseline feature set, a principal component analysis (PCA)-enhanced set, and a factor analysis (FA)–enhanced set capturing latent cost structures. Four regression models (Ridge, Random Forest, XGBoost, and LightGBM) are evaluated using nested cross-validation. XGBoost achieves the best overall performance across all three tracks (Baseline, PCA-enhanced, and FA-enhanced). Among them, PCA-enhanced XGBoost attains the highest predictive accuracy (R² = 0.868; RMSE = 1084.9; MAE = 746.9; MAPE = 0.358; pooled out-of-fold), while Baseline XGBoost yields the lowest MAE (731.4; R² = 0.863). To support transparent decision-making, Shapley Additive exPlanations (SHAP)-based attribution and scenario-based sensitivity analyses are conducted. Results show that project scale and process-level unit costs are dominant cost-drivers, while cloud usage, expert participation, and de-identification requirements exhibit secondary effects. The proposed framework provides an interpretable, data-driven approach to cost information management and decision support for data-intensive AI projects. Full article

The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector is a prototype of a new modular design for a liquid argon time-projection chamber (LArTPC), comprising a two-by-two array of four modules, each further segmented into two optically isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4-tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume—the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon antineutrino events. Full article

In visible-light optical observations for Space Surveillance and Tracking (SST) from ground-based dynamic platforms, attitude disturbances and field-of-view discontinuities frequently undermine interframe geometric consistency, leading to energy diffusion and unstable gain in multi-frame stacking for faint space objects. We propose orbit-prior- guided target-centered stacking (OPG-TCS), a tracking-oriented post-processing method designed to stabilize target energy accumulation and improve enhancement reliability under dynamic observation conditions. OPG-TCS performs frame-wise astrometric calibration using star fields (WCS) and leverages projected orbit priors to predict target pixel locations, enabling local cropping and target-centered alignment/stacking without relying on full-frame geometric consistency. We evaluate OPG-TCS on multiple real-world dynamic-platform sequences and compare it with direct stacking and representative robust baselines. Signal-to-noise ratio (SNR) is used as the primary metric, while auxiliary indicators of peak prominence, energy concentration, and shape consistency are employed to assess robustness across varying stacking depths. The results show that OPG-TCS provides stable enhancement over different frame counts; in representative 50-frame fusions, its relative SNR surpasses direct stacking by 33.7–97.8%. These findings suggest that OPG-TCS offers a practical and robust enhancement strategy for SST-oriented observation of faint space objects, supporting more reliable detection and subsequent tracking analysis. Full article

The construction sector faces environmental challenges related to material consumption, waste generation and energy efficiency. In this context, light-transmitting concrete tiles incorporating recycled glass offer a favourable solution for the construction of lightweight building envelope systems combining circularity, functional performance and design value. This research project developed novel self-compacting high-performance concrete tiles integrating coarse waste-glass aggregates to develop translucent components for use as solar filters. To the authors’ best knowledge there is a gap in the market regarding this type of envelope. Three concrete mixtures were developed, including the reference mix and two waste-glass-based mixtures with different glass contents, colours and nominal size distributions. Concrete tiles with thicknesses between 4 and 20 mm were analysed regarding their overall physical, mechanical, durability and luminous performance. This research paper’s conclusions confirm the suitability of recycled glass concrete tiles for facade applications and support the selection of the minimum viable thickness as a design approach. An optimal thickness of 8 mm was determined, providing the optimal balance between translucency (8–4% light transmittance), structural behaviour (flexural strength > 7 MPa) and durability performance (mass losses < 2.34%). Improving the mechanical performance of slender elements by increasing both the contribution of fibres and matrix–waste bonding are among the future follow-up steps. Full article

The UK faces an urgent challenge to simultaneously accelerate housing delivery and reduce whole-life carbon emissions, yet robust empirical evidence on the carbon performance of modular steel housing remains limited. This study aims to quantify the carbon impacts of a modular light-gauge steel frame social housing dwelling in the UK and to benchmark its performance against contemporary low-carbon construction typologies. A cradle-to-grave life cycle assessment was conducted using primary project data from a real modular housing development, with embodied carbon modelled in One Click LCA and operational energy assessed through SAP 10.2-verified datasets. The results indicate a total whole-life carbon footprint of 91.3 tCO₂e over a 50-year period, with embodied emissions (A1–A3) accounting for 38.2% and operational energy and water use contributing 48.1%. The normalised embodied carbon intensity of 366 kgCO₂e/m² (A1–A5) is comparable to recent high-performing cross-laminated timber buildings, demonstrating that optimised modular steel systems can allow for low-carbon outcomes typically associated with bio-based construction. Sensitivity analysis shows that low-carbon foundation concrete, bio-based insulation, and steel optimisation can reduce upfront emissions by approximately 8–10%. Dynamic energy simulations were also used to assess how different design choices influence operational carbon emissions. This study provides transparent, real-project evidence of the whole-life carbon performance of UK modular light-gauge steel frame housing and identifies practical design strategies for further decarbonisation. The findings support informed decision-making for policymakers, designers, and housing providers seeking scalable, low-carbon residential solutions. Full article

Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and effectiveness during surgery. Surgical instruments are central components of any surgical intervention, yet detecting and locating them during live surgeries remains challenging due to adverse imaging conditions such as blood occlusion, smoke, blur, glare, low-contrast, instrument scale variation, and other artifacts. Methods: To address these challenges, we developed an advanced segmentation architecture termed the frozen-filters-based morphology-aware segmentation network (FFMS-Net). Accurate surgical instrument segmentation strongly depends on edge and morphology information; however, in conventional neural networks, this spatial information is progressively degraded during spatial processing. FFMS-Net introduces a frozen and learnable feature pipeline (FLFP) that simultaneously exploits frozen edge representations and learnable features. Within FLFP, Sobel and Laplacian filters are frozen to preserve edge and orientation information, which is subsequently fused with learnable initial spatial features. Moreover, a tri-atrous blending (TAB) block is employed at the end of the encoder to fuse multi-receptive-field-based contextual information, preserving instrument morphology and improving robustness under challenging conditions such as blur, blood occlusion, and smoke. Datasets focused on surgical instruments often suffer from severe class imbalance and poor instrument visibility. To mitigate these issues, FFMS-Net incorporates a progressively structure-preserving decoder (PSPD) that aggregates dilated and standard spatial information after each upsampling stage to maintain class structure. Multi-scale spatial features from different encoder levels are further fused using light skip paths (LSPs) to project channels with task-relevant patterns. Results/Conclusions: FFMS-Net is extensively evaluated on three challenging datasets: UW-Sinus-surgery-live, UW-Sinus-cadaveric, and CholecSeg8k. The proposed method demonstrates promising performance compared with state-of-the-art approaches while requiring only 1.5 million trainable parameters. In addition, an open-source large language model is integrated for non-clinical summarization of the surgical scene based on the predicted mask and deterministic descriptors derived from it. Full article