Search Results (1,388)

The valorization of agro-industrial residues through fermentation processes represents a sustainable approach to producing high-value bioproducts, such as microbial organic acids and fermentation-derived anti-browning agents, including kojic acid and kojic acid-rich fermented extracts. In this study, melon waste (non-commercial-quality or damaged fruit) was evaluated as an alternative carbon source (whole fruit) for kojic acid (KA) production by Aspergillus oryzae (ATCC 10124) under submerged fermentation. The effects of process variables such as pH, temperature, and nitrogen and carbon availability on KA synthesis were analyzed, and biomass growth and product formation were described using logistic and Luedeking–Piret kinetic models. Under optimal conditions (pH 5.5, 36 °C, 2.5 g/L melon dry matter, 2.5 g/L yeast extract, 100 rpm), KA production reached 1.64 g/L at a final time of 120 h. Kinetic analysis showed moderate fungal growth (μ_max = 0.058 h⁻¹; X_max = 0.81 g/L), with KA formation following a mixed growth-associated pattern as described by the Luedeking–Piret model (α = 1.26 g KA/g X; β = 0.024 h⁻¹), indicating sustained production during the stationary phase. The KA-rich fermented extract was subsequently applied as an anti-browning treatment on spineless prickly pear (Opuntia ficus-indica) cladodes. Short immersion times (0.5–1.0 min) in a 2 g/L KA solution significantly preserved luminosity (L*) and limited total color change (ΔE ≤ 5) during 4 days of storage at 28 °C, compared with water-treated controls, which exhibited accelerated darkening (ΔE ≈ 9–15). Prolonged immersion times induced tissue damage and color deterioration, indicating an optimal exposure window. These results demonstrate the feasibility of valorizing melon waste to obtain a KA-rich extract and support its potential application as a natural anti-browning agent in fresh-cut vegetables within a circular agrifood framework. Full article

Background: Gastrointestinal bleeding (GIB) is a common and serious complication in patients with left ventricular assist devices (LVADs), contributing to significant morbidity, prolonged hospitalization, and increased healthcare costs. We evaluated national trends, demographic disparities, and outcomes of GIB in hospitalized LVAD patients. Methods: We analyzed adult (≥18 years) LVAD hospitalizations in the National Inpatient Sample (2016–2021), identifying internal LVADs using ICD-10-PCS code 02HA0QZ. GIB was defined using ICD-10-CM codes and classified into upper (UGIB) and lower (LGIB) sources. Survey-weighted logistic and linear regression models assessed associations with mortality, length of stay (LOS), and total charges. Subgroup analyses explored sex and racial disparities. Results: Among 20,785 weighted adult LVAD admissions, 9.8% had GIB. Of these, 72.3% had LGIB and 31.0% had UGIB. Patients with GIB were older (59.2 vs. 54.8 years) and more likely to be female (43% vs. 40%) and Black (9.2% vs. 7.8%). GIB was associated with longer LOS (+15.3 days, 95% CI: 12.0–18.5), higher charges (+$316,031, 95% CI: $212,435–$419,627), and greater in-hospital mortality (OR 1.69, 95% CI: 1.25–2.29; p < 0.001). Female patients with GIB had higher odds of mortality (OR 1.37) and increased LOS (+5.6 days), though this was not statistically significant. Racial disparities were evident: Black patients with GIB had longer LOS (+8.9 days), while Asian/Pacific Islander patients had shorter LOS (–23.3 days, p < 0.001). From 2016 to 2021, GIB prevalence rose modestly (from 9.4% to 10.7%, p = 0.33), with no significant change in mortality trends (p = 0.13). Conclusions: GIB complicates nearly 1 in 10 LVAD hospitalizations, with lower GI bleeds being most common. GIB is independently associated with higher mortality, LOS, and costs. Persistent gender and racial disparities highlight the need for targeted strategies to improve outcomes in this high-risk population. Full article

Background/Objectives: Cervical facet joints are a common source of chronic neck pain, yet factors predicting treatment response to pulsed radiofrequency remain poorly defined. This study aimed to identify predictors of treatment success following ultrasound-guided cervical medial branch pulsed radiofrequency in patients with chronic cervical facet joint pain. Methods: This retrospective cohort study included 54 patients with chronic cervical facet joint pain who had positive response to diagnostic block. Pain intensity and functional disability were assessed at baseline and at 1-, 3-, and 6-months post-procedure, with treatment success defined as ≥50% pain reduction at 6 months. Results: The success rate was 35.2%, and multivariate logistic regression identified four independent predictors: presence of paraspinal tenderness on physical examination, shorter pain duration, lower baseline pain intensity, and lower baseline disability. Conclusions: These findings suggest that patients with localized facet joint pathology manifesting as paraspinal tenderness, shorter symptom duration, and lower baseline severity are most likely to benefit from this intervention, supporting early referral and careful clinical selection to optimize treatment outcomes. Full article

Avoiding highway infrastructure construction cost overruns and reducing associated claims and disputes continues to be a challenge in many countries. Research is needed in identifying notable project planning and management deficiencies that are likely to cause cost overruns. The literature suggests numerous potential causes of cost overrun but the clustering of cause variables and relative importance of clusters has not been researched. The research reported here addresses this knowledge gap using predictive models developed with data contributed by several agencies in participating countries and suggests mitigation measures. Following a review of methods and data sources, a methodological framework is advanced that encompasses statistical methods well suited for providing a scientific basis for identifying important clusters of cost overrun variables. Fifty-three completed questionnaires contributed by knowledge experts and experienced managers from Canada, the United States, the Middle East, and Australia met the sample requirements of statistical methods. Starting from 53 variables, the principal component-supported factor analysis method identified clusters of cost overrun variables and their relative importance was inferred with developed logistic regression models. Deeper insights into the causes of cost overruns obtained from this research suggest mitigation measures (e.g., improved qualification and experience of personnel, enhanced planning and design practices, risk analysis of inputs to cost estimation process) that are within reach of managers. The results can enhance infrastructure planning and management practice including a reduction in claims and disputes. Full article

Background: Tuberculosis (TB) outcomes remain suboptimal in high-burden, resource-constrained settings. Clinical and socio-economic factors contribute to loss to follow-up, failure, and mortality, yet their relative importance remains underexplored. Methods: We analyzed a retrospective cohort of patients treated for pulmonary TB in the Eastern Cape, South Africa. Treatment outcomes were dichotomized as success (cured or treatment completed) versus unsuccessful (loss to follow-up, failure, or death), excluding transfers and patients still on treatment. Predictors included age, gender, income, occupation, comorbidities, HIV status, previous treatment history, patient category, and drug resistance status. Regularized logistic regression was used to estimate odds ratios, while the best decision tree model was applied to identify hierarchical risk profiles. Results: Logistic regression demonstrated high accuracy (86%) and identified drug susceptibility, age, income stability, and comorbidity burden as the strongest predictors of treatment success. The decision tree achieved lower accuracy (65%) but improved detection of unsuccessful outcomes, highlighting a clear hierarchy of risk: (1) drug resistance status, (2) age, (3) income source, and (4) comorbidities. Patients with drug-resistant TB, older age, no income or reliance on grants, and coexisting conditions were at the highest risk of poor outcomes. Conclusions: Drug resistance, age, income, and comorbidity burden shape a hierarchical risk profile for TB treatment outcomes in rural South Africa. Logistic regression offered robust overall classification, while the decision tree provided transparent stratification of at-risk groups. These findings underscore the need for integrated clinical and socio-economic support strategies to improve outcomes in high-burden settings. Full article

Additive manufacturing has emerged as a key enabling technology for in-space manufacturing, offering the potential to reduce logistics mass, enhance mission autonomy, and support long-duration exploration. The suppression of gravity-driven phenomena fundamentally alters melt pool dynamics, heat transfer, surface-tension-dominated flow, and defect formation, limiting the direct transferability of terrestrial AM process knowledge to space applications. This paper reviews the current understanding of metallic additive manufacturing process physics under reduced gravity, with emphasis on melt pool behavior, dimensional stability, and in situ monitoring constraints. Approaches for qualification and certification are critically examined, including the applicability of existing AM standards, the role of digital twins and model-based verification, and emerging strategies for space-based validation. Enabling technologies such as autonomous and AI-assisted fabrication, compact hardware architectures, and alternative energy sources are discussed in the context of reliable in-space operation. By synthesizing current developments and identifying key limitations and open challenges, the review provides a roadmap for advancing additive manufacturing toward operational readiness, supporting sustainable exploration, in-space infrastructure development, and long-duration human presence beyond low Earth orbit. Full article

This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value of 0.476225 (p < 0.001), which splits the sample into distinct uncertainty regimes. Logistic regression incorporating this threshold reveals that the joint condition of high opening volume and low IVU (high uncertainty) significantly amplifies the predictive power of initial returns, achieving 63.04% accuracy in the high-uncertainty, high-volume regime. XGBoost further captures complex non-linear interactions, with IVU-related features ranking among the most important predictors and achieving 71.43% out-of-sample accuracy under high-volume, high-uncertainty conditions. A machine learning trading strategy leveraging these predictions yields a total return of 117.99% with a Sharpe ratio of 3.02 over seven years, significantly outperforming benchmarks. Our findings highlight information uncertainty as a critical moderator of intraday momentum and a valuable source of actionable alpha. Full article

Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian Mixture Model (GMM) is adopted based on the Point of Interest (POI) data to determine the clustering centres of the study area. Secondly, a Voronoi diagram is constructed to divide the study area reasonably. Then, a comprehensive feature matrix is constructed by integrating multi-source spatial data and five machine learning models: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). These are then used for training and evaluation. Finally, the GBDT model with the best performance in terms of both the F1 score and the AUC value was selected to predict the location of fire stations in Chengguan District, Lanzhou City. The results demonstrate the GBDT model’s effectiveness in identifying the rationale behind existing fire station locations and predicting potential new locations. It predicts 12 suitable locations for new fire stations, and the suitability of these predicted locations is validated by comparing them with the existing fire station locations, 8 of which are in the same block as existing fire stations in Chengguan District. Adding micro fire stations at four new predicted locations would improve response efficiency. The results of the feature importance analysis show that road accessibility is the primary factor affecting fire station location selection. This study’s proposed method effectively enhances the reasonableness of fire station site selection and provides a basis for planning fire stations in new urban areas in the future. Full article

The global energy transition faces the critical challenge of intermittency in renewable sources, which causes grid imbalances and estimated annual losses of USD 42 billion. Within the framework of circular economy and sustainability, mechanical energy storage (MES) systems—such as compressed air energy storage (CAES) and flywheels—emerge as scalable, long-lived solutions (over 30 years), reducing dependence on fossil fuels by up to 94%. To provide a comprehensive assessment, this study applies a Technology–Economy–Policy (TEP) framework to differentiate the maturity and iteration rates of MES sub-technologies (CAES, flywheels, pumped hydro). Furthermore, it integrates core circular economy indicators—lifespan extension, material efficiency, and multi-vector synergy—to evaluate the sustainability impact of these systems. To assess their impact and evolution, a quantitative bibliometric methodology was applied, analyzing 706 documents from the Scopus database (2010–2025). The study employed tools such as R Studio (Bibliometrix), VOSviewer, and Plotly for co-occurrence mapping, cluster density analysis, and keyword burst detection. Results reveal exponential growth in research, fitted to a logistic model (R² = 0.969), with a projected productivity peak in 2032. A technological shift toward high-efficiency solutions, such as adiabatic CAES (75%) and flywheels (95%), is evident, with grid stability prioritized. Furthermore, artificial intelligence is already applied in 40% of new management models to optimize these hybrid systems. The analysis, which quantitatively identifies underexplored areas such as socio-technical integration and standardized testing protocols, concludes that integrating MES is essential for the sustainability and circularity of the power system, enabling synergy with other vectors such as green hydrogen and fostering scalable business models that strengthen the circular economy in the energy sector. Full article

From 1950 to the present, plastic production and use have increased mainly because plastics possess qualities like stability, light weight, versatility, and decreasing production costs. However, most plastics are not biodegradable, and only a small portion is recycled worldwide. Bioplastics serve as an alternative if they are biodegradable and derived from residual materials, promoting a circular economy. PHB is a polymer with characteristics similar to some commercial plastics. It was discovered in the 1920s and has been examined by researchers and engineers since then due to its potential as a biodegradable bioplastic. Some microorganisms can produce PHB under controlled conditions. In this work, PHB production was analyzed using two strains, Bacillus subtilis and Bacillus megaterium, and two byproducts—whey and glycerol—as substrates and varying the culture media compositions. Both byproducts and both strains are suitable for PHB production; the absence of nitrogen and trace element sources enhances PHB yield. Additionally, bacterial growth, substrate uptake, and PHB production were modeled using logistic growth and the Luedeking–Piret models. Full article

At present, large-scale hazardous chemical logistics warehouses are characterized by complex structural layouts, diverse stored materials, and high operational risks, which pose significant challenges to fire emergency response. The awareness of hazardous material inventory, orderliness, and timeliness of on-site reconnaissance directly determine the efficiency and safety of firefighting and rescue operations. In response to these challenges, this study, based on 77 fire cases involving hazardous chemical logistics warehouses, proposes an evaluation framework that integrates a TOWA–TOWGA hybrid operator with complex network analysis. Accordingly, a fire scene core reconnaissance task identification model is developed. The new model is capable of identifying key reconnaissance tasks while capturing the dynamic evolutionary patterns of fire development across three distinct stages. The research findings demonstrate that identifying the fire’s spread direction, locating accessible water sources, and pinpointing the fire’s ignition point constitute the core tasks throughout the entire fire emergency response cycle. The priority ranking of these core tasks exhibits distinct temporal variability as the fire evolves dynamically. This model enables the accurate identification of key reconnaissance tasks and critical operational pathways, thereby providing robust theoretical support and a solid practical foundation for fire rescue teams to optimize resource allocation strategies and formulate science-based reconnaissance protocols. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

Background: Preterm birth remains a major cause of perinatal morbidity and long-term developmental complications. Existing prediction methods often lack individualized assessment and have limited capability to integrate multi-source maternal–fetal information. This study aims to develop a personalized preterm birth risk prediction model and to construct a visual, interactive digital twin platform that enhances clinical communication and supports early risk identification. Methods: A total of 1157 structured clinical records collected from 2020 to 2024 were preprocessed through automated feature typing, missing-value handling, and normalization. Two complementary machine-learning models—FT-Transformer and Light Gradient Boosting Machine (LightGBM)—were trained and calibrated to produce probabilities. Their outputs were fused using a Stacking Logistic Regression framework to improve prediction stability and calibration. A 3D visualization module was developed using 3ds Max, PyQt6, and PyVista to generate personalized uterine–fetal models based on fetal position, placental location, and Biparietal Diameter (BPD), enabling synchronized display of prediction results. Results: The fused model achieved an AUC of 0.820, PR-AUC of 0.405, a Brier score of 0.040, and an expected calibration error (ECE) of 3.39 × 10⁻³, demonstrating superior discrimination and probability reliability compared with single models. The interactive platform supports real-time data input, risk prediction, and adaptive 3D rendering, providing clear and intuitive visual feedback for clinical interpretation. Conclusions: The integration of machine learning fusion and digital twin visualization enables individualized assessment of preterm birth risk. The system improves model accuracy, enhances interpretability, and offers a practical tool for clinical follow-up, risk counseling, and maternal health education. Full article

Background/Objectives: Hyperbaric Oxygen Therapy (HBOT) involves breathing oxygen at pressures greater than atmospheric levels and is used to treat diverse clinical conditions. However, little is known about the lived experiences and perceived needs of patients undergoing scheduled treatment in multiplace hyperbaric chambers, where nurses play a key role in support, safety, and communication. This study aimed to explore the perceptions, expectations, and needs of patients receiving scheduled HBOT sessions in a multiplace chamber in a hospital setting. Methods: A qualitative phenomenological design was used. Participants were recruited consecutively among adults who had completed at least 10 HBOT sessions and demonstrated adequate cognitive function. Individual semi-structured interviews were conducted between January and March 2023 in locations chosen by participants. Interviews were audio-recorded, transcribed, and validated by participants. Results: Twelve participants (eight men, four women; aged 25–84 years) were included. Four thematic areas emerged: (1) Biopsychosocial lived experiences, including initial uncertainty, physical discomfort such as ear pressure or mask-related issues, and progressive recognition of therapeutic benefits. (2) Interpersonal relationships, highlighting trust, security, and emotional support provided mainly by nurses. (3) Communication experiences, with participants expressing satisfaction but requesting clearer, earlier information on procedures, risks, and expected sensations. (4) Structural and organizational factors, where transportation logistics and treatment scheduling were significant sources of fatigue and discomfort. Conclusions: Patients valued HBOT and perceived notable health improvements, while identifying specific unmet informational and organizational needs. These findings suggest the importance of nurse-led educational interventions to enhance preparation, reduce anxiety, and optimize patient experience during HBOT. Full article

The rapid expansion of e-commerce has significantly influenced consumer purchasing behavior, making user reviews a critical source of product-related information. However, the large volume of low-quality and superficial reviews limits the ability to obtain reliable insights. This study aims to classify Turkish e-commerce reviews as either useful or useless, thereby highlighting high-quality content to support more informed consumer decisions. A dataset of 15,170 Turkish product reviews collected from major e-commerce platforms was analyzed using traditional machine learning approaches, including Support Vector Machines and Logistic Regression, and transformer-based models such as BERT and RoBERTa. In addition, a novel Multi-Transformer Fusion Framework (MTFF) was proposed by integrating BERT and RoBERTa representations through concatenation, weighted-sum, and attention-based fusion strategies. Experimental results demonstrated that the concatenation-based fusion model achieved the highest performance with an F1-score of 91.75%, outperforming all individual models. Among standalone models, Turkish BERT achieved the best performance (F1: 89.37%), while the BERT + Logistic Regression hybrid approach yielded an F1-score of 88.47%. The findings indicate that multi-transformer architectures substantially enhance classification performance, particularly for agglutinative languages such as Turkish. To improve the interpretability of the proposed framework, SHAP (SHapley Additive exPlanations) was employed to analyze feature contributions and provide transparent explanations for model predictions, revealing that the model primarily relies on experience-oriented and semantically meaningful linguistic cues. The proposed approach can support e-commerce platforms by automatically prioritizing high-quality and informative reviews, thereby improving user experience and decision-making processes. Full article