Search Results (56)

Paper-based colorimetric assays offer a low-complexity strategy for transforming enzymatic reactions into measurable optical responses. In this study, individual colorimetric assays for glucose, cholesterol, and dopamine were evaluated using glucose oxidase (GOx), cholesterol oxidase (ChOx), and monoamine oxidase A (MAO-A) immobilized on chitosan macrospheres. Immobilization efficiencies of 79%, 70%, and 70% were obtained for GOx, ChOx, and MAO-A, respectively, with saturation times of 20 min for GOx and ChOx and 10 min for MAO-A. The enzymatic reactions generated concentration-dependent chromatic responses under controlled laboratory conditions. Color measurements were performed under standardized instrumental conditions and expressed using CIE XYZ tristimulus coordinates, while the Euclidean distance relative to the corresponding blank was used as the analytical response. Glucose showed an approximately linear response over the evaluated calibration range, whereas cholesterol exhibited a nonlinear saturation behavior and was described using an exponential saturation model. For dopamine, quantitative linear calibration was restricted to the low-concentration region using the non-blank standards. The limits of detection were 32.01 mg/dL for glucose, 1.62 mg/dL for cholesterol, and 0.00047 µg/mL for dopamine, with corresponding limits of quantification of 96.99 mg/dL, 4.91 mg/dL, and 0.00142 µg/mL, respectively. The results demonstrate the feasibility of combining chitosan-immobilized enzymes, paper-supported chromogenic reactions, and quantitative color analysis for the individual determination of glucose, cholesterol, and dopamine under controlled laboratory conditions. A paper-based microfluidic architecture is additionally proposed as a proof-of-concept for the future integration of the individually characterized assays. Full article

Scan-assisted interocclusal registration may vary with record extent and occlusal support. Using separately fabricated high-hardness vinyl polysiloxane (VPS) records, a fixed bilateral buccal intraoral scanner (IOS) acquisition protocol, optical reference articulations, and position-level three-dimensional analysis, this in vitro study compared prepared-tooth (Hard-Prep) and complete-arch (Hard-Arch) records across three support configurations and assessed a no-record IOS workflow. Ninety VPS-based and 45 no-record IOS acquisitions were analyzed. VPS records were fabricated under a 3.0 kg load; measurements used a 1.0 kg load. Overall three-dimensional deviation (dXYZ) was averaged over five mandibular positions. Log-transformed dXYZ was analyzed by ordinary least squares with Holm-adjusted contrasts. The Hard-Arch/Hard-Prep contrast was not statistically significant under bilateral extended support. Hard-Arch showed higher dXYZ under unilateral extended support (ratio, 1.75; 95% confidence interval (CI), 1.54–1.99; p < 0.001) and unilateral limited support (ratio, 1.58; 95% CI, 1.39–1.80; p < 0.001). In the no-record workflow, unilateral extended support exceeded bilateral extended support (ratio, 1.50; 95% CI, 1.31–1.72; p < 0.001), whereas unilateral limited support was lower than unilateral extended support (ratio, 0.68; 95% CI, 0.59–0.77; p < 0.001). Prepared-tooth records yielded lower deviation in both unilateral configurations, while the no-record workflow varied with support configuration. Full article

Hydrogen-rich superconductors have emerged as promising candidates for achieving high-temperature superconductivity, yet their practical applications are generally limited by the requirement for high external pressures. In this work, taking the Fm-3-XYZ₂H₁₂ structure as a prototype, we constructed a series of LiMgM₂H₁₂ quaternary hydrides and the LiMgZrHfH₁₂ quinary hydride. High-throughput screening indicates that the three hydrides, LiMgZr₂H₁₂, LiMgHf₂H₁₂, and LiMgZrHfH₁₂, are dynamically stable at ambient pressure but thermodynamically metastable. Electron–phonon coupling calculations show that the T_c values of LiMgZr₂H₁₂, LiMgHf₂H₁₂, and LiMgZrHfH₁₂ reach 87.4, 81.2, and 85.2 K at ambient pressure, respectively, all exceeding the boiling point of liquid nitrogen and demonstrating excellent superconducting properties. Further analysis reveals that, compared with the Ga-based parent Fm-3-XYZ₂H₁₂ hydrides, Li substitution markedly reconstructs the electronic-state distribution near the Fermi level and enhances the contribution of H atoms to the density of states near the Fermi level. This promotes the coupling between conducting electrons and high-frequency hydrogen vibrations. Such strong electron–phonon coupling plays a crucial role in their high-temperature superconductivity. These findings provide valuable insights into the theoretical design of high-T_c superconductors under ambient pressure and offer theoretical guidance for future experimental studies in this field. Full article

This paper presents a novel approach for estimating 3D human pose from monocular video sequences in underwater scenarios, tackling the unique challenges posed by water refraction, body occlusion, low image quality and illumination distortion in underwater environments. Leveraging both 2D keypoint extraction and parametric model estimation, our method operates in a two-stage framework including preprocessing and optimization. In the preprocessing stage, a Part Attention Regressor (PARE) is adopted to dynamically estimate SMPL human body parameters, particularly adept at handling occlusions common in underwater scenarios. Additionally, a 2D keypoint detector, employing YOLO for bounding box detection and HRNet for keypoint regression, enhances feature extraction despite underwater image challenges. In the optimization stage, we propose an underwater variational autoencoder (UW-VAE), which adopts a data-driven strategy to learn the biomechanical prior distribution of underwater human poses and implicitly correct unreasonable pose parameters caused by refraction and occlusion. The optimization process incorporates constraints aligning final SMPL models with detected 2D keypoints, minimizing disparity between adjusted and original SMPL models, and ensuring temporal consistency. Furthermore, to address the scarcity of annotated underwater datasets, we build a full pipeline to generate synthetic underwater datasets with complete annotations based on UW-VAE. Experimental results on the SwimXYZ synthetic dataset show that our method achieves 51.60% PCK@0.2 and 80.13% PCK@0.5, outperforming state-of-the-art land-based methods across most stroke categories. Validation on real-world underwater swimming datasets demonstrates improved 2D keypoint accuracy after synthetic-data fine-tuning, which provides a new solution for 3D human motion analysis in underwater sports, biomechanical research and swimming training. Full article

Despite rapid advances in laser display technology, existing evaluation frameworks remain confined to isolated physical metrics, decoupled from human visual perception. This study presents the laser display comprehensive testing system (LD-CTS003), a unified platform integrating physical characterization and visual perceptual assessment. Grounded in opponent-process theory, the system implements a complete color conversion pipeline from display RGB through CIE XYZ and LMS to the Derrington–Krauskopf–Lennie space, linking spectral output to retinal cone responses. The hardware architecture features five-axis precision motion and multi-sensor synchronous acquisition, while the software supports both conventional optical measurements and psychophysical experiments. Static image resolution was evaluated via stripe-pattern modulation analysis across viewing distances, and visual contrast sensitivity was measured using Gabor stimuli under varying luminance and eccentricity. The results demonstrate that reduced viewing distances enhance effective resolution, with text display imposing stricter requirements than image display. Contrast sensitivity functions exhibit band-pass profiles, with luminance and eccentricity strongly modulating achromatic and red–green channels, whereas yellow–violet responses remain relatively robust peripherally. By unifying objective metrology and subjective evaluation, this work establishes a perception-oriented framework for laser display quality assessment, providing a physiologically grounded foundation for display optimization and standard development. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

This study investigates the integration of ABC/XYZ (value-based classification/demand variability classification) inventory classification with demand forecasting models (ETS—Error, Trend, Seasonality, ARIMA—AutoRegressive Integrated Moving Average, Prophet—type of additive model) in a manufacturing enterprise to support sustainable resource management. The research aims to evaluate the inventory structure, demand variability, and forecasting accuracy across different material categories. The results confirm a strong concentration of inventory value in A-class items and significant differences in forecast accuracy across ABC/XYZ segments. While AX items generally exhibit lower forecast errors, notable exceptions highlight the need for additional diagnostic analysis. The findings demonstrate that integrating classification and forecasting improves inventory decision-making, reduces excess stock, and supports sustainable resource utilization. The proposed approach provides practical guidance for optimizing inventory management in industrial environments. Full article

Ensuring uninterrupted access to essential medicines in public healthcare systems is a persistent challenge with clinical, economic, and environmental implications. Oncology services are particularly vulnerable to stockouts, which compromise therapeutic continuity and increase reliance on urgent procurement with high carbon and waste footprints. This study proposes a risk-informed, data-driven framework for pharmaceutical inventory governance in a high-complexity public oncology hospital in Chile, aligning with sustainability goals and green supply chain principles. Using operational data from 2023–2024, we integrate descriptive analytics, ABC–XYZ segmentation, and a continuous-review (s, Q) policy extended through a Logistic Risk Index (LRI) that consolidates demand variability, supply performance, and clinical-economic criticality. Empirical analysis reveals strong expenditure concentration in AX/AY segments and significant misalignment between institutional and analytically derived parameters. A Monte Carlo simulation N = 1000 runs per scenario) compares baseline, adjusted, and fully risk-informed policies under stochastic demand and lead-time conditions. Results show that the risk-informed configuration reduces stockout exposure by up to 46%, improves fill rates (93.1% → 96.4%), and shortens replenishment delays, while maintaining total logistic cost stability. Critically, urgent orders decrease from 27.4 to 14.8 per year, avoiding an estimated 630 kg CO₂ emissions and 25 kg of packaging waste annually. These findings demonstrate that resilience, efficiency, and sustainability are not competing objectives but can be jointly achieved through integrated analytics and governance. The proposed approach offers a scalable blueprint for public health systems seeking to transition from reactive inventory management toward anticipatory, transparent, and sustainability-oriented decision-making, contributing to SDG 3 (health and well-being) and SDG 12 (responsible consumption and production). Full article

In recent years, there has been a significant negative impact on the sustainability of supply chains for the delivery of small batch cargo, caused by crisis situations. Therefore, it is important to develop a modern methodology to reduce uncertainty in the delivery of small batch cargo, especially when considering a flexible inventory management system. This study proposes an integrated approach to inventory management, consisting of three elements: an updated ABC-XYZ structure of inventory formation analysis with criteria that determine stability; an additive mathematical model for calculating inventory management costs; and the development of a regression model for operational forecasting of inventory management costs, based on the number of end customers, unit cost and batch size. A comparison of regressions showed the advantage of the power model over the linear one. The main advantage of the study is the proposed mathematical and regression models for the operational calculation of inventory management costs, considering the uncertainty factors that determine the sustainability of the supply chain. This approach will be of interest to trading enterprises, allowing them to make flexible decisions in inventory management in the event of various disruptions in small batch cargo supply chains. Full article

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field synchronous machines (WFSMs) have problems with their rotor brushes and slip-ring assembly, wherein the assembly starts to malfunction in the long run. Furthermore, recently, some brushless WFSM topologies have been investigated to eliminate the problems associated with rotor brushes and slip rings, but they have either a high cost due to a double-inverter, or low flux regulation freedom due to a single inverter (−i_d). The proposed nine-switch topology achieves a low cost by using a single inverter with nine switches and excellent flux control through three variables (−i_d, i_q, and i_f), making it highly suitable for wide-speed applications. In the proposed topology, the machine’s armature winding is divided into two sets of coils: ABC and XYZ. A 12-slot and 8-pole machine stator is wound with armature winding coils ABC and XYZ, creating six terminals for injecting currents and two neutrals from each ABC and XYZ coil set. The current to the ABC and XYZ coils is supplied by a nine-switch inverter. The inverter is specially designed to supply rated currents to the ABC winding coils and half of the rated current to the XYZ winding coils. The number of turns of the ABC and XYZ winding coils are kept the same so they produce the same winding function. However, the current in the XYZ winding coils is half compared to that of the ABC winding coils, which creates an asymmetrical airgap magnetomotive force (MMF). The asymmetrical airgap MMF contains two working harmonics, i.e., fundamental MMF for torque production and an additional sub-harmonic MMF component for rotor field brushless excitation. The rotor field is controlled by the difference in current of the two armature winding coils: ABC and XYZ. The proposed topology is validated through theoretical analysis and finite element simulations of electromagnetic and flux regulation. A 2D finite-element analysis is performed to verify the idea. The proposed topology is capable of establishing a 9.15 A dc current in the rotor field winding coil, which consequently generates a torque of 7.8 N·m with a 20.30% torque ripple. Rotor field flux regulation was analyzed from the stator ABC and XYZ coils current ratio ζ. The ratio ζ is analyzed as 2 to 1.3; subsequently, the inducted field currents were 9.15 A dc to 4.8 A dc, respectively. Full article

This study examines the influence of build orientation on the mechanical behavior of 316 stainless steel components fabricated by selective laser melting (SLM). Additively manufactured tensile specimens produced in different build orientations were experimentally analyzed and compared with reference specimens obtained from conventionally hot-rolled material and laser-cut to identical geometries. Uniaxial tensile testing combined with digital image correlation (DIC) was employed to evaluate the mechanical response and full-field strain evolution. Microstructural features were investigated using scanning electron microscopy (SEM), while phase composition was assessed by X-ray diffraction (XRD). The results reveal a pronounced orientation-dependent mechanical anisotropy in the SLM specimens, reflected in variations in yield strength, ultimate tensile strength, and ductility. Specimens loaded perpendicular to the build directions exhibited higher strength but reduced ductility compared to those loaded parallel to the build direction, whereas the rolled material showed a more isotropic mechanical response. Although the XYZ and XZY samples feature similar deposition patterns, the XRD analysis revealed a the existence of a $\left[220\right]$ texture. Thus, the mechanical performances of XZY specimens are about $10\%$ lower compared to XYZ printed samples. The stress maximum–strain curves were extrapolated from the true data using the Swift model. The section dedicated to numerical modeling includes a failure model based on the traixility. The numerical models were validated for the range $\eta \in \left[0.33\dots 0.45\right]$ specific to uniaxial tension. Fractographic observations further confirmed the correlation between build orientation, microstructural features, and fracture behavior. The present study provides a multiscale experimental framework linking processing conditions, microstructure, and mechanical response in additively manufactured stainless steel. Full article

Background: In the context of increasing supply chain complexity, efficient inventory management has become important in enhancing the performance of logistics systems and sustaining the competitiveness of companies. Real-time visibility, tracking, and control over stock levels ensure responsiveness, reduce waste, and support strategic decision-making. Decision support systems that integrate demand analysis with inventory policies play a pivotal role in improving operational efficiency. This paper addresses the need for more efficient stock management to optimize purchasing and inventory costs within a manufacturing environment. Methods: Production planning processes were analyzed to determine material requirements, and a representative product was selected. The study involved ABC classification based on the average annual stock value of purchased parts, complemented by an XYZ analysis to evaluate demand variability. Afterwards, stock management policies were tested, namely, continuous and periodic review models. Each item was assessed to determine the most suitable inventory management method based on its consumption profile. Results: A comparison with the company’s existing approach revealed that for 9 out of the 13 materials studied, the application of stock management models led to improvements. Conclusions: The results show a potential cost reduction of 33% for the nine materials to which stock policies were successfully applied. Full article

In the digital age, generative artificial intelligence (GenAI) development has brought about structural transformations in higher education. This study examines how students’ regular use of artificial intelligence tools brings a new active player into the educational process. This is an “uninvited assistant” that changes traditional models of teaching and learning. This study was conducted using the following standard methods: bibliometric analysis, student survey using an electronic questionnaire, primary processing and graphical visualization of empirical data, calculation of statistical indicators, t-statistics, and z-statistics. As the results of the bibliometric analysis show, the evolution in the perception and integration of artificial intelligence within higher education discussions, as evidenced by the comparison of network visualizations from 2020 to the present, reveals a significant transformation. Based on a quantitative survey of 1197 undergraduate students in five Eastern European countries, this paper proposes a conceptual shift from the classic two-dimensional (2D) model of higher education services based on university teacher–student interactions to a three-dimensional (3D) model that includes artificial intelligence as a functional third player (an uninvited assistant). Statistical hypothesis testing confirms that students need AI and regularly use it in the learning process, facilitating the emergence of this new player. Based on empirical data, this study presents a hypothetical 3D model (X:Y:Z), where the Z-axis reflects the intensity of AI use. This model challenges traditional didactic frameworks and calls for updating educational policies, ethical standards, and higher education governance systems. By merging digital technologies and social change, the results provide a theoretical and practical basis for rethinking pedagogical relationships and institutional roles in the digital age. Full article

The code distance is a critical parameter of quantum stabilizer codes (QSCs), and determining it—whether exactly or approximately—is known to be an NP-complete problem. However, its upper bound can be determined efficiently by some methods such as the Monte Carlo method. Leveraging the Monte Carlo method, we propose an algorithm to compute the upper bound on the code distance of a given QSC using fully decoupled belief propagation combined with ordered statistics decoding (FDBP-OSD). Our algorithm demonstrates high precision: for various QSCs with known distances, the computed upper bounds match the actual values. Additionally, we explore upper bounds for the minimum weight of logical X operators in the Z-type Tanner-graph-recursive-expansion (Z-TGRE) code and the Chamon code—an XYZ product code constructed from three repetition codes. The results on Z-TGRE codes align with theoretical analysis, while the results on Chamon codes suggest that XYZ product codes may achieve a code distance of $O\left(N^{2/3}\right)$, which supports the conjecture of Leverrier et al. Full article

Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems. Full article