Search Results (502)

Objectives: This study evaluates the protocol-level image quality and quantitative diffusion metrics of a clinically implemented deep-learning–reconstructed readout-segmented echo-planar imaging protocol with water-excitation spectral fat saturation (DL-rs-EPI with WEXfs) compared with conventional rs-EPI using spectral attenuated inversion recovery (SPAIR) at 3 T. Methods: Overall, 80 patients underwent breast magnetic resonance imaging (MRI) with both conventional rs-EPI with SPAIR and DL-rs-EPI with WEXfs protocols (b-values: 0, 800, and 1200 s/mm²). ROI-based relative image-quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast, were assessed at b = 800 and b = 1200 s/mm²; apparent diffusion coefficient (ADC) values were calculated using multi-b-value data. Fat suppression, background diffusion signal, lesion conspicuity, and artifact severity were qualitatively evaluated. A temperature-controlled diffusion phantom (CaliberMRI) was scanned; ADC values were compared with reference values at 24 °C. Results: DL-rs-EPI with WEXfs demonstrated higher ROI-based relative SNR estimates (b800: 5.79 vs. 5.28; b1200: 5.41 vs. 4.94; p < 0.001) and CNR estimates (b800: 3.35 vs. 3.12, p = 0.024; b1200: 3.67 vs. 3.37, p = 0.001), with unchanged lesion contrast. Tumor ADC values were comparable between protocols, whereas normal fibroglandular tissue ADC values were slightly higher, and ADC contrast increased with DL-rs-EPI with WEXfs. Phantom ADC values from both protocols closely matched reference values at 24 °C, without significant differences. DL-rs-EPI with WEXfs demonstrated more homogeneous fat suppression and reduced background diffusion signal, with comparable lesion conspicuity and artifact severity. Conclusions: The combined DL-rs-EPI with WEXfs protocol demonstrated improved qualitative and relative quantitative image quality while preserving tumor ADC measurements. As a protocol-level evaluation, these composite improvements support its clinical feasibility for high-quality breast DWI without implying the isolated effect of DL reconstruction alone. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

Cordyceps militaris is a high-value medicinal mushroom with growing demand in functional-food and nutraceutical markets, yet practical frameworks for small-scale, family-operated cultivation remain limited. This study presents an integrated technical and economic feasibility analysis of small-scale Cordyceps production under two scenarios: a one-room setup (Scenario 1) and a two-room configuration with a shared processing area and staggered scheduling (Scenario 2). Both use consistent biological, operational, and market assumptions with no hired labor, and the analysis covers capital expenditure (CapEx), operating costs (OpEx), profitability, payback, and break-even thresholds, complemented by sensitivity analysis of parameters such as biological efficiency and contamination rates. Both scenarios were technically and financially viable. Scenario 1 achieved a net present value (NPV) of $1761, an internal rate of return (IRR) of 10%, a 4.7-year discounted payback, and a 133% five-year return on investment (ROI); Scenario 2 attained an NPV of $85,437, a 66% IRR, a 1.6-year payback, and a 366% ROI. Because gross margins were consistent across scales, the expansion’s advantage stemmed from more efficient CapEx amortization rather than improved unit profitability. Cordyceps cultivation emerges as a viable family-operated, small-scale enterprise that can diversify family income, generate supplementary or primary earnings, and support urban and rural livelihoods. Full article

This study presents regionalisation of the Generalised Extreme Value (GEV) distribution by adopting L moments and Bayesian Generalised Least Squares (BGLS) regression. This uses data from 88 gauged catchments in New South Wales, Australia. The regional GEV distribution is compared using the Parameter Regression Technique (PRT) and Quantile Regression Technique (QRT) under fixed-region and Region-of-Influence (ROI) approaches. Results indicate improved regional flood estimation, capturing spatial variability and reducing uncertainty in flood quantile estimation through the ROI approach compared to fixed regions. The mean flood statistic shows a higher spatial heterogeneity in the fixed region approach, while the ROI approach more effectively reduces uncertainty and enhances predictive performance. For fixed regions, PRT achieves a median relative error (Rer%) of 34–40% and a relative root mean square error (RRMSE%) of 59–65%, compared with QRT’s Rer% of 40–63% and RRMSE of 59–94%. In the ROI framework, both techniques yield similar Rer% (31–40%), though QRT-ROI exhibits slightly reduced RRMSE. Full article

This contribution presents a simulation-based approach for optimizing injection molding processes using digital twins. It combines surrogate modeling via response surface methodology (RSM) with the evolutionary algorithm NSGA-II to efficiently capture complex relationships between process parameters and objectives. A key element is the adaptive enhancement of the training dataset within the decision-relevant region of interest (ADEROI) by a modified greedy max–min algorithm. This strategy closes data gaps, improves model accuracy in the potentially optimal region, and directs additional simulations to informative areas. Leave-one-out (LOO) and hold-out (HO) cross-validations show strong root mean square error (RMSE) and $R^2$ values for deformation, shrinkage, cycle time, and mass. NSGA-II converges after 403 generations and results in 191 Pareto-optimal solutions, which are consolidated into preference-consistent operating points. These points make trade-offs between analyzed objectives’ deformation, shrinkage, and cycle time explicit for process pre-design. Preferred solutions are identified through weighted sums of normalized objectives and inversely mapped process parameters. Their agreement with the physics-based digital twin at the hundredths level supports the plausibility of the selected operating points within the investigated simulation-based workflow. A retrospective benchmark against a scaled single-stage LHS baseline shows that ADEROI achieves ROI-equivalent point density with fewer simulation runs for the investigated case, reducing the estimated runtime by $39.1\%$ and resulting in a $1.64\times$ speed-up. The quantitative validation is limited to one thin-walled PP keyholder component; further geometries, mold layouts, and polymer materials are required to empirically assess generalizability. Full article

Background/Objectives: Apparent diffusion coefficient (ADC) calculated from diffusion-weighted MRI (DWI) can predict tumor response to neoadjuvant chemotherapy for breast cancer. However, obtaining consistently adequate image quality in breast DWI can be challenging, and the effect of image quality on ADC’s predictive performance is unclear. The objective of this study was to evaluate inter-reader variability in image quality assessment and the effect of DWI image quality on the predictive performance of ADC. Methods: This multi-institutional study included 428 patients. Two readers assessed three DWI image quality factors—fat suppression, artifacts, and signal-to-noise ratio (SNR). Inter-reader agreement was estimated using Fleiss’ Kappa. The percent change in tumor ADC from pretreatment (T0) to early treatment (T1) was used to predict pathologic complete response (pCR), assessed at surgery. Results: Out of 428 patients, 134 were excluded (missing pCR [n = 17]; missing/incorrect DWI [n = 23]; inability to define region-of-interest [ROI, n = 94]) and 294 were included in the analysis. Kappa coefficients were estimated as: 0.47 (95% confidence interval [CI]: 0.42, 0.52) for fat suppression, 0.54 (0.50, 0.59) for artifact, and 0.38 (0.32, 0.44) for SNR. The AUC of ADC calculated from DWI with adequate (high or medium at both time points) image quality was 0.61 (95% CI: 0.52, 0.702), while it was 0.68 (95% CI: 0.53, 0.83) from DWI with inadequate image quality at either T0 or T1. The p-value for the difference in AUCs was 0.45. Conclusions: The inter-reader agreement was moderate to fair across all three quality categories. When a manually delineated tumor ROI was possible, no statistically significant difference in ADC predictive performance was observed between the quality-adequate and quality-inadequate cohorts; still, both were predictive of pCR. Furthermore, no statistically significant differences were observed in inter-reader agreement or ADC predictive performance between 1.5T and 3T scanners. These findings are clinically relevant to the use of ADC as an imaging biomarker in real-world conditions. Full article

Background/Objectives: In laparoscopic and robot-assisted surgery, bleeding may rapidly impair operative-field readability and procedural safety. In the broader Robin Heart teleoperation framework, interpretation of such events is relevant not only for scene understanding but also as a potential prerequisite for future safety-oriented supervisory functions under communication-degraded conditions. The aim of this study was to assess whether a deep learning model for blood segmentation could provide outputs suitable for preliminary image-level temporal characterization of visible blood-region behavior in laparoscopic video. Methods: A U-Net-based binary blood-segmentation model was implemented in-house in PyTorch and evaluated on three paired image–mask datasets: a simulated bleeding dataset prepared under controlled laboratory conditions, an internal operative laparoscopic dataset, and an external-domain subset derived from the public GynSurg dataset. Segmentation performance was assessed using 5-fold cross-validation and reported using the Dice coefficient and Intersection over Union (IoU). Training dynamics were analyzed using training and validation loss and Dice curves. Additional baseline comparisons were performed on the internal operative dataset using U-Net++ and DeepLabV3+. Temporal analysis was performed on selected video fragments, including a low-motion reference sequence without active bleeding progression, internal bleeding-related sequences, and external-domain sequences, using mask-derived descriptors and auxiliary optical-flow-based motion descriptors computed after camera-motion compensation within the detected blood-related ROI. Results: In 5-fold cross-validation, the U-Net-based model achieved Dice coefficient and IoU values of 0.915 ± 0.012 and 0.851 ± 0.019 on the simulated dataset, 0.856 ± 0.013 and 0.756 ± 0.025 on the internal operative dataset, and 0.707 ± 0.053 and 0.570 ± 0.056 on the external-domain GynSurg subset, respectively. On the internal operative dataset, the proposed model performed comparably to U-Net++ and slightly above DeepLabV3+ under the same cross-validation protocol. The temporal descriptor set differentiated low-motion reference behavior, more spatially coherent progression, rapid coherent expansion, and dynamic or motion-active progression profiles. Peak dA/dt reflected abrupt visible blood-area expansion, temporal IoU described mask stability over time, and optical-flow-based descriptors provided additional information on local motion activity within the detected blood-related ROI. Conclusions: The results support the feasibility of combining deep-learning-based blood segmentation with temporal and optical-flow-based descriptors for exploratory image-level characterization of visible blood-region behavior in laparoscopic video. Within the Robin Heart development pathway, such descriptors may, in the future, serve as candidate components of image-analysis support modules for safety-oriented teleoperative scenarios. At this stage, they should be interpreted as exploratory image-derived indicators rather than clinically validated markers of bleeding severity. Full article

Objective: To quantify and map bone mineral density (BMD) at the bases of human finger phalanges using computed tomography (CT) with a calibration phantom and to compare BMD both between and within digits. Methods: Ten cadaveric hands (H1 to H10) were CT scanned with a Model 3 CT Calibration Phantom (Mindways). All data were processed in the Horos software (Version 4.0.0) and the regions of interest (ROIs) at each phalangeal base were delineated. Hounsfield Units (HU) were converted to BMD (mg/cm³) per the phantom framework. Descriptive statistics and repeated-measures ANOVA analyses were performed for each digit and corresponding phalangeal level (proximal, middle, distal). Inter-digital comparisons were performed at corresponding phalanx levels and intra-digital variations were analyzed within digits across phalangeal levels. Results: Mean BMD varied across digits and phalangeal levels. At the proximal phalanx base, the thumb and index fingers exhibited the highest values, whereas at the middle phalanx base the middle and ring fingers demonstrated the highest mean BMD values. At the distal phalanx base, the little finger demonstrated the highest BMD value, while the lowest value was observed at the distal phalanx of the index finger. Intra-digital analysis revealed distinct distribution patterns: BMD decreased distally in the thumb and index fingers, peaked at the middle phalanx in the middle and ring fingers, and was highest distally in the little finger. Repeated-measures ANOVA demonstrated statistically significant intra-digital differences in the thumb and index fingers, whereas no statistically significant inter-digital differences were observed across corresponding phalangeal levels. Conclusions: CT-based, phantom-calibrated BMD mapping at the bases of the phalanges demonstrates substantial intra-digital variability and descriptive inter-digital differences. These site-specific findings may provide additional information relevant to implant selection and preoperative planning for fixation in phalangeal fractures and tendon- or ligament-to-bone insertion injuries in hand surgery. Full article

Background/Objectives: Diagnosing hemangioblastomas using magnetic resonance imaging (MRI) is challenging, especially when the tumors appear as solid posterior fossa masses. This study aimed to evaluate the diagnostic performance of perfusion MRI and identify the most useful quantitative features for differentiating hemangioblastomas from other posterior fossa tumors. Methods: Forty-five posterior fossa tumors were analyzed, including 18 hemangioblastomas (HB group) and 27 non-hemangioblastoma tumors (NHB group; 8 metastatic brain tumors, 6 pilocytic astrocytomas, 5 malignant lymphomas, 4 glioblastomas, 2 medulloblastomas, and 2 other tumors). All patients underwent 3.0-T MRI. Arterial spin labeling (ASL) was used to calculate the relative tumor blood flow normalized to the contralateral gray matter. Dynamic susceptibility contrast (DSC) imaging was used to obtain regional cerebral blood flow, regional and corrected cerebral blood volume (CBV), and permeability index (K2) values. Regions of interest (ROIs) were placed within the contrast-enhancing areas. Results: The relative ASL values and corrected CBV were significantly higher in hemangioblastomas than in other tumors (p < 0.001). Relative ASL showed the highest diagnostic performance (sensitivity, 100%; specificity, 93.3%). Conclusions: Non-contrast ASL showed strong diagnostic performance for identifying posterior fossa hemangioblastomas and may serve as a practical alternative to contrast-enhanced DSC, although ROI placement can be challenging in very small mural nodules. Full article

This study proposes an integrated terrestrial laser scanning (TLS)-based workflow for quantitatively and spatially assessing the relative geometric condition of exterior wall surfaces. The workflow consists of point-cloud acquisition, ROI definition, reference-plane estimation, signed-depth computation, grid-based spatial aggregation, specimen-based validation, and real exterior wall application. Rather than introducing a fundamentally new point-cloud processing algorithm, the main contribution lies in integrating established processing steps into a consistent surface-based assessment procedure and extending deviation evaluation from simple numerical summaries to spatial interpretation. A 3D-printed validation specimen with designed defect depths of 1, 3, 5, and 7 mm was used for quantitative validation. Among 136 designed defects, 123 ground-truth-mapped ROIs were evaluated, resulting in an MAE of 0.795 mm, RMSE of 1.168 mm, and P95 error of 2.511 mm. A RANSAC threshold-based sensitivity analysis confirmed that the final refined reference plane and major signed-depth statistics remained stable within the tested threshold range. The workflow was further applied to a real exterior wall dataset with 29,933,332 strict-ROI points, yielding a mean signed depth of 2.448 mm, median of 2.691 mm, RMSE of 9.956 mm, P95 of 17.121 mm, and maximum value of 90.827 mm. High-deviation regions with an absolute centered signed depth of 15 mm or greater occupied 28.218 m², corresponding to 10.62% of the valid analysis area, and were distributed across 57 connected clusters. These results indicate that the proposed workflow can support both quantitative deviation assessment and spatial interpretation of high-deviation regions, while the real exterior wall results should be interpreted as a relative geometric assessment and feasibility demonstration rather than absolute accuracy validation or structural damage assessment. Full article

Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without explicitly accounting for variability associated with breast density. Breast cancer diagnosis from mammography is strongly influenced by dataset composition, annotation variability, and breast density distribution, factors that are rarely controlled in current AI evaluations. We introduce Mass-Bench, a clinically balanced and harmonized multi-dataset benchmark that integrates CBIS-DDSM, INBREAST, VINDr-Mammo, and DMID under a unified canonical schema, with standardized ACR density and BI-RADS encoding. Using a leakage-controlled and distribution-aware evaluation protocol, density-stratified mass detection and lesion-centered regions of interest (ROIs) classification were assessed across datasets. YOLO-based detection models achieved peak area under the curve (AUC) values up to 0.943; however, performance systematically degraded with increasing ACR density, revealing limitations that are often masked in imbalanced evaluations. By enforcing clinically representative density distributions, Mass-Bench provides a more reliable estimation of localization performance, which directly impacts downstream clinical tasks. In this context, binary ACR classification achieved F1-scores up to 0.976, while binary BI-RADS discrimination reached accuracies up to 0.93. However, multi-class classification remained more challenging, showing increased sensitivity to dataset heterogeneity and contextual information. These findings demonstrate that conventional evaluations may overestimate robustness, particularly in dense breast categories, and highlight the importance of density-aware benchmarking for developing reliable and clinically applicable AI systems in mammography. Full article

Reinforcement learning agents for financial trading typically optimize reward functions that directly map profit and loss to learning signals, without accounting for the agent’s own decision certainty. This paper investigates whether modulating reward signals by a confidence estimate, without modifying network architecture, training procedures, or data pipelines, can meaningfully improve trading performance. We formalize five lightweight confidence estimation methods, each targeting a distinct uncertainty dimension: critic agreement (value estimation), temporal direction consistency (behavioral stability), state novelty (distributional familiarity), action magnitude stability (position sizing), and state-transition surprise (environmental predictability). Using a Twin Delayed Deep Deterministic Policy Gradient agent trained on hourly OHLCV data for Bitcoin, Litecoin, and Ethereum over five years encompassing diverse market regimes, we conduct a controlled experiment in which the confidence method is the sole variable across 18 experimental conditions. State novelty achieves the strongest improvement, raising mean test-period ROI from 5.7% to 24.9%, increasing Sharpe ratio (SR) from 0.34 to 1.57, and reducing maximum drawdown from 28.0% to 15.0% across the three cryptocurrencies. Four of the five methods reach statistical significance at $p<0.05$ on all assets; only state-transition surprise, the sole method requiring an auxiliary network, fails to distinguish itself from the baseline due to signal saturation. The proposed confidence-aware reward-shaping framework is plug-and-play, algorithm-agnostic, and directly applicable to other RL-based trading systems. Full article

This study comparatively evaluates the effectiveness of remarketing strategies under digital transformation in Uzbekistan’s service (tourism and hospitality) and manufacturing (textile) sectors, grounded in the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Using a sequential explanatory mixed-methods design, 280 enterprises (140 per sector) from four regions of Uzbekistan were surveyed, integrating quantitative analysis (OLS regression, t-test, χ²-test, PLS-SEM) and Monte Carlo simulation (20,000 iterations) with qualitative in-depth interviews (n = 32). The textile sector exhibited higher but more volatile returns (ROI = 82.1%; CV = 0.18), whereas the tourism sector achieved more stable yet lower returns (ROI = 48.3%; CV = 0.11) (t(278) = −22.84; p < 0.001; Cohen’s d = 2.73). AI-based personalization was positively associated with ROI (β = 0.28, p < 0.001) and with reduced revenue volatility through an indirect pathway (indirect effect = 5.04, 95% CI [4.10, 6.00]), with significantly stronger associations in the textile sector (Δ = 1.64, p < 0.05). This study contributes to digital marketing theory by demonstrating sector-specific heterogeneity in AI personalization mechanisms, providing empirical evidence of the infrastructure–ROI variability relationship in a transition economy, and demonstrating the value of integrating Monte Carlo–based uncertainty analysis with mixed-methods evidence as a robustness device. The findings carry direct implications for sustainable economic development in transition economies: by demonstrating how sector-specific digital marketing strategies are linked to and can enhance the long-term viability and resource efficiency of enterprises, this study contributes to advancing Sustainable Development Goal 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). Full article

Electricity purchase price variation can influence the economic feasibility of residential distributed generation, particularly in regulated markets where grid electricity prices and export compensation conditions affect investment decisions. This study evaluates the impact of flat electricity purchase price scenarios on the techno-economic viability of residential grid-connected energy systems in Santo Domingo Tehuantepec, Oaxaca, Mexico, using HOMER Pro. The analysis considers PV, wind generation, diesel generation, converter, and grid connection as candidate components, while evaluating three residential demand profiles of 11.26, 30.00, and 83.30 kWh/day and 10 electricity purchase price scenarios ranging from 3.45 to 5.00 MXN/kWh. The objective is to identify the electricity purchase price values at which the optimal architecture changes from conventional grid-only supply to PV/converter/grid adoption under the evaluated case study assumptions. The results show that grid-only supply remains the least-cost option from 3.45 to 4.20 MXN/kWh for all demand profiles. At 4.25 MXN/kWh, HOMER Pro selects PV/converter/grid configurations for the medium- and high-demand profiles, while the low-demand profile remains grid-only. At 4.30 MXN/kWh, PV/converter/grid also becomes optimal for the low-demand profile. At 5.00 MXN/kWh, ROI reaches 11.0% for the three residential demand profiles, while payback decreases to 6.5 years for the low- and medium-demand profiles and 6.4 years for the high-demand profile. The wind turbine and diesel generator were not selected in the optimal configurations, despite being included as candidate technologies. These findings provide a practical case study indicator of the electricity purchase price levels at which residential PV-grid adoption becomes economically competitive under flat purchase price scenarios and zero export compensation. Full article

Background: This study evaluates the return on investment (ROI) in new transport equipment using a purpose-built graphical user interface (GUI), addressing whether acquiring additional vehicles for peak demand periods is economically viable compared to optimizing the existing fleet. The research focuses on agricultural product transportation—specifically potatoes—across four key routes. Methods: A system dynamics (SD) methodology was applied, combining simulation and data analysis through a GUI that enabled the adjustment of key variables, including operating costs, yields, and transportation expenses. Results: The analysis revealed notable differences in costs and profitability across the studied routes. Variables such as diesel costs and fuel efficiency proved particularly influential on outcomes. The GUI demonstrated clear value as a visualization tool, enhancing comprehension of simulated scenarios and supporting strategic decision-making. Conclusions: Investing in new transport equipment can be profitable under specific operational and economic conditions, providing a solid foundation for expansion and optimization decisions. Beyond its immediate operational contribution, the study offers a replicable profitability analysis model applicable to future projects within the company. Full article