Search Results (1,236)

Extreme summarization generates very short summaries, typically a single sentence, answering the question “What is the document about?”. Although large language models perform well in text generation, fine-tuning them for summarization often requires substantial computational resources that are unavailable to many researchers. In this study, we present an effective method for instruction-tuning open decoder-only large language models under limited GPU resources. The proposed approach combines parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA), with quantization to reduce memory requirements, enabling training on a single consumer-grade GPU. We fine-tuned a pre-trained decoder-only model on the XSum dataset using an instruction-following format. Experimental results demonstrate that the proposed decoder-only approach achieves competitive performance on the XSum dataset under strict GPU memory constraints. On the full test set, the proposed 2G–1R pipeline attains ROUGE-1/2/L F1 scores of 46.0/22.0/37.0 and a BERTScore F1 of 0.917, outperforming the individual generator models in lexical overlap and semantic similarity. Evaluation was conducted using traditional overlap-based metrics (ROUGE) and semantic metrics, including BERTScore and G-Eval. While remaining competitive in ROUGE compared to strong encoder–decoder baselines, the pipeline consistently produces summaries with higher semantic quality. These findings demonstrate that large decoder-only language models can be efficiently fine-tuned for extreme summarization on limited consumer-grade hardware without sacrificing output quality. Full article

Background/Objectives: Red blood cell (RBC) transfusion is frequently used to treat anemia of prematurity, yet bedside metrics that capture its short-term impact on oxygenation stability are limited. We assessed whether pulse oximetry histogram-derived oxygen saturation (SpO₂) exposure changes after transfusion and whether responses differ across clinical subgroups. Methods: This prospective observational cohort included preterm infants born <32 weeks’ gestation who received a standardized RBC transfusion (15 mL/kg). Continuous SpO₂ histograms quantified the percentage of monitored time spent in hypoxemia (<85%), normoxemia (86–95%), and hyperoxemia (≥96%) during four intervals: 24 h pre-transfusion and 24, 48, and 72 h post-transfusion. Repeated-measures and subgroup analyses (BPD, sex, birth weight < 1000 g) were performed. Results: Thirty-three infants were analyzed (gestational age 29.4 ± 2.1 weeks; birth weight 1220.6 ± 316.9 g). Hemoglobin increased from 8.6 ± 1.1 to 11.7 ± 1.0 g/dL (p < 0.001). Cohort-level histogram shifts were modest: normoxemia increased from 68.4 ± 12.1% to 72.6 ± 11.4% at 24 h (p = 0.18), hypoxemia decreased from 10.3 ± 6.5% to 6.6 ± 4.8% (p = 0.09), and hyperoxemia remained stable (21.3 ± 9.2% to 20.8 ± 8.5%; p = 0.44). Infants with BPD and those <1000 g showed persistently higher hypoxemia and/or hyperoxemia at 72 h compared with counterparts. Exploratory ROC analyses showed modest discrimination of 24 h hypoxemia for ROP (AUC 0.71) and 72 h hyperoxemia for BPD (AUC 0.74). Conclusions: RBC transfusion corrected anemia but did not produce a consistent cohort-level improvement in SpO₂ histogram stability. Histogram metrics may help characterize heterogeneous oxygenation responses and support hypothesis generation for individualized monitoring strategies. Full article

Finding Noether symmetries for time-dependent damped dynamical systems remains a significant challenge. This paper introduces a complete geometric algorithm for determining all Noether point symmetries and first integrals for the general class of Lagrangians $L=A\left(t\right)L_0$, which model motion with general linear damping in a Riemannian space. We derive and prove a central Theorem that systematically links these symmetries to the homothetic algebra of the kinetic metric defined by $L_0$. The power of this method is demonstrated through a comprehensive analysis of the damped Kepler problem. Beyond recovering known results for constant damping, we discover new quadratic first integrals for time-dependent damping $\varphi \left(t\right)=\gamma /t$ with $\gamma =-1$ and $\gamma =-1/3$. We also include preliminary results on the Noether symmetries of the damped harmonic oscillator. Finally, we clarify why a time reparameterization that removes damping yields a physically inequivalent system with different Noether symmetries. This work provides a unified geometric framework for analyzing dissipative systems and reveals new integrable cases. Full article

Semitransparent perovskite photovoltaic (sPV) covers offer an attractive route for agrivoltaics, but their spectrally selective transmittance must be validated on plants cultivated under panel or in simulated conditions. Here, an AVA–MAPI perovskite module transmission profile was replicated using a programmable multi-channel LED platform and compared with a Reference McCree-adapted LED spectrum at identical photon flux density. Two lettuce cultivars (Lactuca sativa L.; ‘Canasta’ and ‘Trocadero’) were grown hydroponically in a light-sealed phytotron for 30 days (300 μmol m⁻² s⁻¹; 16/8 h photoperiod) under uniform temperature and humidity. Leaf gas exchange was quantified by fitting photosynthetic light-response curves, and plant performance was concurrently evaluated through growth metrics, biomass partitioning, and pigment-related traits (chlorophyll a/b, total carotenoids). The perovskite-emulated spectrum measurably reshaped net CO₂ assimilation across the PAR domain—yielding higher A_N at selected irradiances in post hoc contrasts—yet these physiological shifts did not translate into differences in leaf area, shoot or root biomass, or pigment concentrations—demonstrating spectral plasticity and agricultural compatibility of field-characterized perovskite transmission spectra. Overall, perovskite-emulated light sustained agronomically equivalent lettuce performance under moderate irradiance, supporting the feasibility of semitransparent perovskite PV covers, while underscoring the need for validation under natural sunlight. Full article

Sedentary behavior contributes to obesity and metabolic dysfunction, yet few interventions individualize exercise intensity using fuel-based metrics such as the respiratory exchange ratio (RER; VCO₂/VO₂). This study investigated the effects of metabolically guided walking combined with whole-food, plant-based nutrition on body composition and metabolic outcomes in sedentary overweight and obese women. Forty-four women mean age 43 years; BMI 30.1 kg·m⁻²) were randomized to low-intensity continuous training (LICT; RER ≈ 0.75), moderate-intensity intermittent training (MIIT; RER ≈ 0.85), or high-intensity continuous training (HICT; RER ≈ 0.95). Following a 2-week dietary lead-in with an individualized ~200 kcal·day⁻¹ energy deficit, participants completed an 8-week RER-guided walking program (5 sessions·week⁻¹; 15–50 min·session⁻¹). Assessments included air-displacement plethysmography (BodPod) body composition, resting metabolic rate and substrate utilization, and oxygen uptake at the first ventilatory threshold (VT1). Data were analyzed using ANCOVA, mixed-factorial ANOVA, and Pearson correlations. Percent body fat decreased significantly across participants (p < 0.0001, η² = 0.827), with MIIT demonstrating the most favorable integrated outcomes. MIIT elicited the largest reductions in total body mass (−11.2%), fat mass (−25.9%), and percent body fat (−17.1%), alongside improvements in VT1 VO₂ (Δ = 1.487 ± 0.895 L·min⁻¹; p = 0.038). Resting respiratory quotient (RQ) declined in LICT and MIIT but increased in HICT, corresponding with increased fat oxidation in LICT and MIIT and reduced fat oxidation in HICT. Changes in RQ were significantly associated with changes in percent body fat (r = 0.316, p = 0.039). Metabolically guided moderate-intensity intermittent walking combined with whole-food, plant-based nutrition produced the most consistent improvements in adiposity, substrate utilization, and submaximal fitness, supporting the public-health feasibility of a community-deliverable, substrate-informed walking prescription. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article

This study focused on determining the conditions leading to variance in the measurements of an antenna array capable of measuring the direction of electromagnetic waves. The payload of the study is a cross-array of antennas that is able to measure direction through array beamforming and angle of arrival (AOA) technology. Starting from the modeling of satellite kinematics (in terms of the satellite’s position and attitude combined with its relative position with respect to an electromagnetic wave emitter located on Earth’s surface), this study provides the mathematical fundamentals to identify potential cases that lead to divergence in the estimation variance for the position of a signal emitter. The numerical and analytical predictions, conducted through an evaluation of the Cramér–Rao lower bound (CRLB) metrics, were on the azimuth, elevation, and broadside angles through the generation of errors in the attitude with Monte Carlo simulations. Recent advancements in the miniaturization of electronics make these studies of particular interest for a new set of technological demonstrators equipped with payloads composed of antenna arrays. Applications of interest include Earth-scanning missions, with exemplary cases of search-and-rescue operations or the spectrum monitoring of jamming in the E1/L1 band for the GNSS. Full article

Background: Age-related declines in respiratory muscle strength and ventilatory efficiency can impair functional capacity and metabolic health in older adults. Inspiratory muscle training (IMT) has been proposed as a practical intervention to counteract these changes, yet its systemic effects remain unclear. This study aimed to examine the effects of short-term IMT on functional capacity, diaphragm thickness, and liver tissue characteristics in healthy elderly men. Methods: Thirty community-dwelling men aged 60–80 years were randomly assigned to an IMT or control group. The IMT group performed four weeks of breathing exercises using a POWERbreathe^® device at 40% of maximal inspiratory pressure, with a weekly 10% increase in pressure. Pre- and post-intervention assessments included the six-minute walk test (6MWT), diaphragm thickness and liver density via computed tomography, and quality of life (QoL; SF-12). Results: Four weeks of inspiratory muscle training significantly improved diaphragm thickness (11.7%), fatty liver density (FLD) (+16.7%), and six-minute walk performance (+5.3%), with large time × group effects favoring the IMT group. While the physical quality of life showed modest, comparable improvements, mental health outcomes demonstrated a moderate, time-dependent improvement without a significant group-by-time interaction. Conclusions: Short-term IMT improved diaphragmatic function and functional capacity in older men and was associated with favorable changes in a liver-related biomarker; however, given that only a single liver-related metric was assessed, these findings should not be interpreted as evidence of overall improvements in liver health. Full article

Metal Injection Molding (MIM) enables complex orthodontic-bracket geometries but can introduce surface and geometric discontinuities that act as initiation sites for crevice and pitting corrosion. The effect of acidic, kombucha-like exposure on corrosion and repassivation was assessed for MIM-316L brackets relative to a commercial comparator, and the coupling between surface quality (roughness and wettability) and localized damage at scanning electron microscopy (SEM)-identified hot-spots was examined. Kombucha was characterized by pH and titratable acidity. Surfaces were characterized by SEM, areal roughness metrics (R_a, S_a, S_z, and A2), and wettability by sessile-drop goniometry. Electrochemical behavior in artificial saliva was measured using open-circuit potential and cyclic potentiodynamic polarization (ASTM F2129/G59), and a qualitative magnetic check was included as a pragmatic quality-assurance screen. Exposure in kombucha reduced breakdown and repassivation potentials and increased passive current density, with the strongest effects co-localizing geometric discontinuities. Commercial brackets exhibited markedly poorer surface quality (notably higher S_z), amplifying acidity-driven susceptibility. These findings indicate that, under acidic challenges, surface/geometry quality dominates corrosion behavior; non-magnetic-phase compliance and simple chairside screening (e.g., magnet test), alongside tighter manufacturing controls on roughness and edge finish, should be incorporated into clinical and industrial quality assurance (QA). Full article

Curcumin, a natural polyphenol derived from Curcuma longa L., exhibits diverse biological activities including anti-inflammatory, anticancer, and antioxidant effects, making it a versatile candidate for food, feed, pharmaceutical, and cosmetic applications. However, its industrial application is hindered by low bioavailability, poor water solubility, and high production costs. This review comprehensively summarizes the latest advances in curcumin’s physicochemical properties, production routes (phytoextraction, chemical synthesis, and microbial biosynthesis), and wide applications. Compared with existing reviews, this work emphasizes quantitative benchmarking of production methods (yield, productivity, and environmental metrics), critical evaluation of application feasibility including regulatory hurdles and clinical evidence, and actionable future directions for industrial scalability. We systematically analyze the advantages, limitations, economic and environmental trade-offs of each production route, and highlight recent innovations in bioavailability enhancement and metabolic engineering. This review aims to provide a holistic theoretical and technical framework for accelerating curcumin’s sustainable development and commercialization in high-value products. Full article

Water scarcity and inefficient irrigation practices are major challenges for modern protected agriculture systems. This study designs, implements, and experimentally validates a neural network-based irrigation control strategy in an industrial programmable logic controller (PLC) for a drip irrigation system operating in a laboratory-scale micro-tunnel greenhouse. The objective is to evaluate the real-time performance of an intelligent controller under practical operating conditions and to quantify its impact on water use efficiency and crop growth compared to a conventional on–off strategy. The neural network is trained using 1039 data samples, divided into training (70%), validation (15%), and test (15%) datasets, and is implemented in the PLC to regulate soil moisture through a proportional valve. Experimental validation is carried out over 67 days using a serrano chili pepper (Capsicum annuum L.) crop. Both controllers operate simultaneously under identical environmental and operating conditions. Performance is evaluated using soil moisture stability metrics, including mean squared error (MSE), mean absolute error (MAE), and standard error (SE), water consumption, and crop growth indicators prior to harvest. Results show that the neural network controller achieves higher soil moisture regulation accuracy (MSE = 3.2159%, MAE = 0.7560%, SE = 0.00001687%) and reduces the average daily water consumption per plant by 50.18% compared with the on–off controller. In addition, the absolute growth rate increases by 26.42%, with statistically significant differences. These results demonstrate that neural network-based control can be effectively implemented on industrial hardware and provide tangible benefits for water-efficient and precision irrigation systems. Full article

The traditional cost estimation process in construction involves extracting information from diverse data sources and relying on human intuition and judgment, making it time-intensive and error-prone. While recent advancements in large language models offer opportunities to automate these processes, their effectiveness in cost estimation tasks remains underexplored. Prior studies have investigated LLM applications in construction, but there is a lack of studies that have systematically evaluated their performance in cost estimation or proposed a framework for systematic evaluations of their performance in cost estimation and ways to enhance their accuracy and reliability through prompt engineering. This study evaluates the performance of pre-trained LLMs (GPT-4o, LLaMA 3.2, Gemini 2.0, and Claude 3.5 Sonnet) for conceptual cost estimation, comparing zero-shot prompting with a modular chain-of-thought framework. The results indicate that zero-shot prompting produced incomplete responses with an average confidence score of 1.91 (64%), whereas the CoT framework improved accuracy to 2.52 (84%) and achieved significant gains across BLEU, ROUGE-L, METEOR, content overlap, and semantic similarity metrics. The proposed modular CoT framework enhances structured reasoning, contextual alignment, and reliability in estimation workflows. This study contributes by developing a conceptual cost estimation framework for LLMs, benchmarking baseline model performance, and demonstrating how structured prompting improves estimation accuracy. This offers a scalable foundation for integrating AI into construction cost estimation workflows. Full article

Rapid urbanization in China has driven annual food waste production to 130 million tons, posing severe environmental challenges for anaerobic digestate management. To resolve trade-offs among drying efficiency, resource recovery (fertilizer/fuel), and carbon neutrality by optimizing the biodried product (BDP) recycling ratio (0–15%), six BDP treatments were tested in 60 L bioreactors. Metrics included drying kinetics, product properties, and environmental–economic trade-offs. The results showed that 12% BDP achieved a peak temperature integral (514.13 °C·d), an optimal biodrying index (3.67), and shortened the cycle to 12 days. Furthermore, 12% BDP yielded total nutrients (N + P₂O₅ + K₂O) of 4.19%, meeting the NY 525-2021 standard in China, while ≤3% BDP maximized fuel suitability with LHV > 5000 kJ·kg⁻¹, compliant with CEN/TC 343 RDF standards. BDP recycling reduced global warming potential by 27.3% and eliminated leachate generation, mitigating groundwater contamination risks. The RDF pathway (12% BDP) achieved the highest NPV (USD 716,725), whereas organic fertilizer required farmland subsidies (28.57/ton) to offset its low market value. A 12% BDP recycling ratio optimally balances technical feasibility, environmental safety, and economic returns, offering a closed-loop solution for global food waste valorization. Full article

Crop production in the desert Southwest of the United States, as well as in other arid and semi-arid regions, requires tools that provide accurate crop evapotranspiration (ET) estimates to support efficient irrigation management. Such tools include the web-based OpenET platform, which provides real-time ET data generated from six satellite-based models, their Ensemble, and a field-based system (LI-710, LI-COR Inc., Lincoln, NE, USA). This study evaluated simulated ET (ET_SIM) of cotton (Gossypium hirsutum L.) derived from OpenET models (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop), their Ensemble approach, and LI-710. Field data were utilized to estimate cotton ET using the soil water balance (SWB) method (ET_SWB) from June to October 2025 in Gila Bend, AZ, USA. Four evaluation metrics, the normalized root-mean-squared error (NRMSE), mean bias error (MBE), simulation error (S_e), and coefficient of determination (R²), were employed to evaluate the performance of OpenET models, their Ensemble, and the LI-710 in estimating cotton ET. Statistical analysis indicated that the ALEXI/DisALEXI, geeSEBAL, and PT-JPL models substantially underestimated ET_SWB, with simulation errors ranging from −26.92% to −20.57%. The eeMETRIC, SIMS, SSEBop, and Ensemble provided acceptable ET estimates (22.57% ≤ NRMSE ≤ 29.85%, −0.36 mm. day⁻¹ ≤ MBE ≤ 0.16 mm. day⁻¹, −7.58% ≤ S_e ≤ 3.42%, 0.57 ≤ R² ≤ 0.74). Meanwhile, LI-710 simulated cotton ET acceptably with a slight tendency to overestimate daily ET by 0.21 mm. A strong positive correlation was observed between daily ET_SIM from LI-710 and ET_SWB, with S_e and NRMSE of 4.40% and 23.68%, respectively. Based on our findings, using a singular OpenET model, such as eeMETRIC, SIMS, or SSEBop, the OpenET Ensemble, and the LI-710 can offer growers and decision-makers reliable guidance for efficient irrigation management of late-planted cotton in arid and semi-arid climates. Full article

Background/Objectives: Digoxin is a cardiotonic agent with a narrow therapeutic window and a high risk of toxicity. The current clinical use is based on an empirically FDA-recommended regimen which has wide dosing ranges, introducing the risk of inappropriate dosing and related adverse events. This study aims to develop a physiologically based pharmacokinetic (PBPK) model to characterize digoxin pharmacokinetics in adult and pediatric patients with heart failure, and then to evaluate the FDA-recommended regimen. Methods: The PBPK model was initially developed in healthy adults using PK-Sim^®. Then, it was translated to adults with heart failure by incorporating disease factors. Next, it was further translated to pediatrics by scaling age-related parameters. Finally, through two-step translations, the model was used to evaluate current dosing regimens to inform safety and effectiveness based on observing predicted trough concentrations at a steady state. Results: This PBPK model has strong predicting ability, where observed concentrations and key PK metrics (C_max, AUC_0-t) were within 0.5–2.0-fold of predictions in healthy adults, adults with heart failure, neonates, and infants. The model prediction work on the evaluation of recommended dosing regimens from the FDA shows that the current regimen may not achieve the lowest boundary of the therapeutic window (0.5–2 ng/mL) in neonates (0–30 days), whereas infants (1–2 months) and children (<18 years) are generally good within it. Conclusions: This PBPK model explained major physiological and pathological contributors to differences in digoxin pharmacokinetics across populations and showed good performance in pediatric extrapolation. It also pointed out the shortage of empirical dosing regimens for such a drug with a narrow therapeutic window. The model may assist in optimizing the pediatric dosing strategies of digoxin, and suggests that current neonatal dosing regimens need refinement. Full article