Search Results (1,503)

This study presents a comprehensive evaluation of sorbent injection to mitigate sodium emissions in a 250 MW_e cyclone-fired boiler using lignite coal. Using historical boiler operational data, a computational fluid dynamics (CFD) model was validated and simulations were subsequently conducted to identify optimum sorbent injection locations for maximizing dispersion within the boiler cross-section and limiting sorbent temperatures to avoid deactivation. Data from the literature were used to guide sorbent injection rates and target sorbent particle sizes. Subsequent field demonstrations with the injection of a commercially available sorbent achieved a 60–80% reduction in the gas phase sodium, which was visually corroborated by reduced deposition on heat exchanger probes placed inside the boiler as well as by data on ash composition as a function of size. Furthermore, a diffusion-kinetic model, incorporating alkali vapor (NaOH) capture and subsequent sorbent deactivation, was developed and integrated into the CFD simulations as a post-processing tool and tested against the field demonstration data. Additional bench-scale testing was conducted with a range of sorbents as part of tool development for selecting from locally available sorbent sources. These bench-scale tests indicated a definite shift in the aerosol particle size distribution (PSD) toward a coarser range and depletion in the ultra-fine sizes, confirming the capture of vapor phase sodium species by the sorbents. Notably, in these tests, the sorbents remained effective even when they became molten, suggesting the potential for more convenient and cost-effective injection strategies. Full article

Mercury contamination in aquatic ecosystems is a worldwide issue due to the health risks associated with eating contaminated fish. The present study was carried out to measure the mercury (Hg) levels in different tissues of locally harvested fish and to assess the potential human health risks associated with their consumption. Fifty-two fish samples (northern pike (Esox lucius), walleye (Sander vitreus), and white bass (Morone chrysops)) were dissected, and tissue samples were individually homogenized and measured for total mercury using a Milestone DMA-80 tri-cell analyzer. Among the species studied, the highest mean Hg concentrations in the muscles and kidneys were observed in northern pike, while the highest Hg concentrations in the livers and gills were noted in white bass. The liver consistently showed the highest mercury accumulation across all species. The Hg levels in almost 50% of the northern pike muscle tissue samples exceeded the limit (0.5 μg/g) set by the European maximum permissible concentration for foodstuffs. Correlation analysis revealed a strong positive correlation between the Hg concentration in the muscles with both the weight and length of walleye and white bass. Human health risk assessment was estimated by the maximum allowable daily fish consumption (CRlim), maximum allowable monthly fish meal consumption (CRmm), individual risk assessment, and the target hazard quotient (THQ). The average monthly consumption limit (CRmm) ranged from two to four meals for adults and one to two meals for children. Individual risk assessment showed potential health risks associated with the regular consumption of northern pike and white bass, followed by walleye, for both adults and children. The mean hazard quotient (THQ) values exceeded one for all species in both adults and children, which indicates potential health risks associated with fish consumption. Across all species studied, children experienced higher risk than adults, suggesting greater susceptibility to potential health effects from the consumption of the studied fish. Lastly, the mercury levels should be regularly monitored, especially in piscivorous fish like northern pike, for pollution control and human health protection. Full article

Background/Objectives: Reliable biomarkers for longitudinal surveillance of neuroendocrine tumors (NETs) remain an unmet clinical need. Chromogranin A (CgA), the most widely used circulating biomarker, is limited by low sensitivity, substantial biologic variability, and poor concordance with radiologic progression. NETest2.0^® is a blood-based multigene transcriptomic liquid biopsy designed to dynamically assess NET biologic activity. This study compared serial NETest2.0^® measurements with CgA for monitoring disease progression in a real-world registry cohort. Methods: Patients with histologically confirmed NETs enrolled in the RegisterNET program (NCT02270567) who had paired blood samples and contemporaneous clinical assessment were included. NETest2.0^® scores were derived from quantitative RT-PCR analysis of a 51-gene transcript panel and expressed on a 0–100 scale. Serum CgA levels were measured using standard clinical immunoassays. Imaging-based disease assessment was performed using CT, MRI, and/or ⁶⁸Ga-somatostatin receptor PET/CT with RECIST 1.1 criteria applied where appropriate. Longitudinal percentage changes (Δ) between sequential measurements were evaluated using predefined NETest2.0^® thresholds and compared with the conventional CgA threshold (>50%). NETest2.0 Δ thresholds of >0% and >5% were evaluated a priori: >0% as a high-sensitivity threshold capturing any upward transcriptomic drift, and >5% as a more conservative threshold intended to reduce minor biological or analytical fluctuation. Receiver operating characteristic (ROC) analysis, operating characteristics, multivariable analysis (MVA), and logistic regression analysis (LRA) were performed. Results: A total of 191 patients were analyzed. Exploratory ROC analysis demonstrated superior discrimination for progression using serial NETest2.0^® changes compared with changes in CgA (AUC: 0.893 vs. 0.538; p < 0.0001). In the primary surveillance analysis, NETest2.0^® thresholds of >0% and >5% achieved AUCs of 0.860 (95% CI: 0.803–0.906) and 0.822 (95% CI: 0.760–0.873), respectively, both significantly superior to CgA (AUC: 0.553, 95% CI: 0.480–0.625; both p < 0.0001). NETest2.0^® >0% demonstrated the highest sensitivity (86.4%), whereas NETest2.0^® >5% achieved the optimal balance of sensitivity (70.5%), specificity (93.9%), and overall accuracy (88.5%). In multivariable and logistic regression analyses, changes in NETest2.0^® were the strongest independent predictor of progression (all p < 0.0001; odds ratios: 52.99–61.26), whereas CgA did not significantly contribute to progression prediction. Conclusions: Serial NETest2.0^® assessment significantly outperformed CgA for monitoring NET disease activity and progression. These findings support the integration of NETest2.0^®into molecularly informed surveillance strategies to complement imaging and improve longitudinal monitoring of patients with NETs. Full article

Mycelium-bound composites (MBCs) have emerged as a promising class of biofabricated materials that integrate fungal hyphal networks with lignocellulosic substrates to form lightweight, biodegradable structures without synthetic adhesives. Despite rapid growth in the field, the current literature remains fragmented, with inconsistent methodologies and widely varying reported material properties. This review advances the field by moving beyond descriptive synthesis toward a quantitative and conceptual integration of existing studies. We systematically analyze how key fabrication variables—including fungal species, substrate composition, growth conditions, and post-processing parameters—govern density, porosity, and mechanical performance. A process–structure–property (PSP) framework is proposed to combine these relationships and explain discrepancies across studies. We highlight the dominant role of densification and moisture conditioning in determining compressive strength, often outweighing species-level effects. A comparative synthesis of reported data reveals significant variability in compressive strength (0.05–1.2 MPa) and elastic modulus, attributable to inconsistencies in sample preparation, testing protocols, and environmental conditioning. To address this, we identify critical gaps in standardization and propose actionable testing protocols and reporting guidelines for reproducibility. Furthermore, we assess the technology readiness level (TRL) of MBC systems and distinguish between laboratory-scale innovations and commercially viable processes. While hybridization strategies and biofunctional applications offer promising avenues, their maturity varies widely. This work provides a decision-oriented framework for MBC design and a roadmap for transitioning these materials from experimental systems to scalable, standardized, and application-ready biomaterials. Full article

The aim of this study was to quantify the effects of implement type, field condition, and gear stage on tractor traction performance under representative field operating conditions using full-scale field-measured data. Field experiments were conducted in five fields using a 78-kW agricultural tractor equipped with a moldboard plow, chisel plow, and subsoiler, and traction performance was evaluated based on the tractive coefficient (TC) and tractive efficiency (TE), both computed from directly measured drawbar pull and axle power. An effect size (η²) analysis revealed a clear and consistent hierarchy among the three factors: field condition was dominant (η² = 91.6% for TC and 88.2% for TE), followed by implement type (67.8% for TC but only 11.3% for TE), whereas gear stage was minor (≤5.9%). TC and TE were highest in the firm loam field and lowest in the wet loamy sand field, with clearly separated, non-overlapping distributions across fields. The pronounced TC–TE asymmetry of implement type shows that the two indicators capture complementary aspects of traction and must be evaluated jointly. Measured TC distributions were broadly consistent with soil texture and moisture-based net TC reference bands, and the measured TE values deviated from the ASAE D497.4 reference values depending on the soil texture and moisture content, demonstrating that traction performance can vary substantially even within the same soil texture class. These results indicate that soil texture, moisture content, and cone index must be considered jointly when evaluating tractor traction performance, and are expected to contribute to tractor design optimization and the establishment of field condition-based work management standards. Full article

The goal of synthesizing more environmentally friendly high-water-absorbing gels (HWAGs) is to reduce the consumption of petroleum resources. We also aimed to make rational use of plant straw. Reed straw (RS) was modified using strong irradiation. Acrylamide (AM) and acrylic acid (AA) were grafted onto the cellulose for graft copolymerization reactions. The cellulose and gels were analyzed using infrared spectroscopy (IR), scanning electron microscopy (SEM), and thermogravimetric (TG) analysis. To reduce the crystallinity of the RS, the optimal irradiation dosage is 96 kGy. Single-factor and orthogonal experiments were conducted to determine the optimal reaction conditions for IRSC-HWAG: the monomer ratio m(AA):m(AM) was 1.5; The material ratio of (m_(AA+Am):m_(IRSC)) was 9:1; The neutralization degree of AA was approximately 90%; The irradiation dose was 5 kGy, with a dose rate of 2.0 kGy/h; the crosslinking agent dosage was 1.2%. The absorption rate of deionized water (Q_d) by the gels was approximately 1160 g/g; the absorption rate of salt water (Q_s) by the gels was approximately 99 g/g. A water-retention experiment demonstrated the IRSC-HWAG’s superior water-retention properties. These properties were compared with those of resins synthesized using pure chemical reagents. This experiment provides valuable reference data for the further development of plant-based water-retention agents. Full article

The global sugar industry is facing increasing challenges due to climate variability, sustainability requirements, and the need for improved operational efficiency. These pressures are driving the search for advanced technological solutions to enhance productivity and resource management. Artificial intelligence (AI) has already demonstrated significant potential across various agricultural sectors; however, a comprehensive evaluation of AI applications across the entire sugar industry value chain from crop cultivation to industrial processing and supply chain management remains limited. This review provides a detailed assessment of the current state of AI and internet of things (IoT) implementation in the sugar beet industry. It examines key applications, including precision agriculture for sugarcane and sugar beet cultivation, intelligent monitoring systems for early disease detection, and AI-driven decision support tools for resource optimization. In addition, the study explores the role of AI in sugar manufacturing processes, where machine learning and data-driven models are used to optimize milling operations, improve product quality control, and enable predictive maintenance of industrial equipment. AI technologies are also shown to enhance supply chain efficiency through improved demand forecasting, logistics optimization, and real-time data analytics. Monitoring volatile organic compounds (VOCs) is becoming increasingly important in sugar beet and sugarcane storage. Microbial activity during storage and fermentation can release VOCs such as ethanol, which act as early indicators of crop degradation and spoilage. Detecting these gases using modern gas sensors enables continuous monitoring of storage conditions and crop health. When sensor data is integrated with AI and IoT systems, it can be analyzed in real time to identify early signs of microbial activity, improve storage management, and optimize processing decisions. Such intelligent monitoring systems have the potential to reduce losses and enhance overall efficiency in the sugar production chain. Full article

Vertebral fractures are among the most common skeletal injuries and present significant clinical and biomechanical challenges, particularly in older adults and individuals with low bone density. Accurate prediction of vertebral mechanical response and failure under varying loading conditions is essential for improving understanding of spinal injury mechanisms. This study develops a density-dependent viscoelastic analytical model to predict the stiffness and fracture force of human vertebral specimens subjected to different compression rates. The vertebral body is represented as a composite structure consisting of a cortical shell and a trabecular core. Cortical bone is modeled as a linear elastic material, whereas trabecular bone is described using a Kelvin–Voigt viscoelastic formulation. Density-dependent constitutive relationships are incorporated for the elastic modulus and viscous coefficient of trabecular bone. Unknown material parameters are identified through optimization using the Nelder–Mead algorithm, based on experimental compression data from cadaveric vertebral specimens tested under quasi-static and dynamic loading conditions. The calibrated model reproduced the overall trend of specimen-to-specimen mechanical variation observed experimentally. Predicted stiffness values were in reasonable agreement with measured data. Fracture force predictions showed moderate agreement for dynamically tested specimens (R² = 0.60), which improved to R² = 0.88 after exclusion of one statistically identified outlier. Compared with a purely linear elastic formulation, the proposed viscoelastic model demonstrated modest improvement in stiffness prediction and more substantial improvement in fracture force prediction. These findings indicate that incorporating density-dependent viscoelastic effects improves representation of vertebral mechanical behavior, particularly at higher loading rates. Owing to its simplicity and computational efficiency, the proposed model requires only limited imaging input and may be useful for future biomechanical investigations, rapid screening, and injury risk prediction. Full article

Background: Outcomes in chronic myeloid leukemia (CML) remain heterogeneous despite effective BCR::ABL1 tyrosine kinase inhibitors (TKIs). Somatic mutations in epigenetic regulators, particularly additional sex combs-like 1 (ASXL1), have been implicated in adverse prognosis, but their clinical impact in CML has not been systematically defined. Methods: A systematic review was conducted using CINAHL, EMBASE, MEDLINE Ultimate, and PubMed from inception through August 2025. A total of 1339 records were identified; the eligible studies included adult and pediatric patients with chronic and advanced-phase (accelerated or blast) CML. After duplicate removal and screening, 11 studies met the inclusion criteria; these included adult patients only and were included in a qualitative synthesis and meta-analysis. ASXL1 mutation status was assessed using validated molecular methods. The outcomes included the molecular response, cytogenetic response, survival, and treatment resistance. Random-effects models were used to calculate the pooled odds ratios (ORs) with 95% confidence intervals (CIs). Statistical heterogeneity was assessed using the I² statistic. Results: Across the included studies, ASXL1 mutations were detected in approximately 15% of patients. At 12 months, patients with ASXL1 mutations had significantly lower odds of achieving a major molecular response (MMR) compared with ASXL1-wildtype patients (OR 0.29; 95% CI 0.16–0.51; p < 0.0001; I² = 30%). No statistically significant difference was observed in the complete cytogenetic response (CCyR) (OR 0.30; 95% CI 0.02–5.31; p = 0.41; I² = 68%). Compared with patients harboring other non-ASXL1 somatic mutations, an ASXL1 mutation was not associated with a significant difference in MMR (OR 0.49; 95% CI 0.23–1.05; p = 0.067; I² = 0%). Conclusions: ASXL1 mutations may be associated with an inferior molecular response to TKI therapy in CML, supporting their role as an adverse prognostic biomarker. These findings highlight the potential value of incorporating myeloid mutation profiling into future CML risk-stratification strategies. Full article

Bacteriophages are increasingly investigated as tools for studying and manipulating microbial communities in cattle. However, phage isolation remains challenging because of host specificity, microbial ecosystem differences, and the lack of optimized screening approaches. The objectives of this study were to (i) optimize the phage-screening method for microbial samples obtained from different cattle body sites, (ii) isolate lytic phages against key bovine pathogens and commensal bacteria, and (iii) characterize the isolated phages and their bacterial hosts. A total of 1214 samples from different cattle body sites (n = 1194) and environmental sources (n = 20) were screened using 13 phage detection methods, including one high-throughput approach. Eighty-three phages were isolated, primarily from ruminal fluid (59), feces (15), vaginal (7) and nasopharyngeal swabs (1), and fetal ruminal fluid (1). The bacterial hosts inhibited by these phages were from 29 genera, with Bacillus (34), Escherichia/Shigella (8), Shouchella (5), Corynebacterium (4), and Lysinibacillus (4) being the most common. No phages were identified against bovine pathogens including Trueperella pyogenes, Mannheimia haemolytica, Pasteurella multocida, or Moraxella bovis. Method 12 demonstrated the highest efficiency in phage recovery, particularly from ruminal samples. The successful recovery of bacteriophages from gastrointestinal, reproductive, respiratory, and fetal bovine samples demonstrates the utility of the optimized screening methods for isolating phages from diverse cattle-associated microbial ecosystems. Further studies are needed to refine these approaches to improve the recovery of phages targeting bovine pathogens. Full article

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

The global transition toward renewable energy has intensified interest in dispatchable low-carbon sources that can support reliability-critical infrastructure in smart grid systems. Data centers represent one of the fastest-growing electricity loads globally, yet their compatibility with biomass-based energy systems as a dispatchable renewable source within smart grid architectures remains poorly understood. This study presented a comprehensive review of biomass power generation, data center energy management, and smart grid integration, drawing on a corpus of 347 peer-reviewed sources. A staged analytical design separated demand characterization from supply evaluation, ensuring that data center energy requirements emerged independently of supply-side assumptions. Using Latent Dirichlet Allocation topic modeling validated with BERTopic and VOSviewer network analysis, the study identified four distinct thematic clusters and found no single topic spanning data center reliability requirements, biomass supply dynamics, and smart grid integration simultaneously, a pattern that points to an underexplored cross-domain space in the literature. A demand–supply–grid alignment framework was introduced to illustrate compatibility conditions across temporal resolution, reliability requirements, and grid management dimensions. The alignment framework and illustrative simulation developed here are offered as analytical starting points to guide future engineering and empirical investigation rather than as demonstrations of operational readiness. An illustrative application demonstrated that biomass feedstock logistics constraints create persistent availability gaps at data center operational timescales, suggesting that supply chain resilience and grid-mediated buffering are likely necessary conditions for viable integration, a proposition that warrants empirical validation through full-scale engineering studies. The findings indicate that integration constraints reflect temporal and operational misalignment rather than technological infeasibility, providing a new analytical perspective for evaluating renewable energy integration in reliability-critical digital infrastructure. Full article

Highway–rail grade crossing (HRGC) incidents remain a persistent safety concern due to repeated interactions between roadway users and rail operations under varying environmental and operational conditions. Existing studies rely on raw incident counts or partial exposure measures that can be influenced by differences in infrastructure exposure and do not account for spatial dependence, limiting consistent comparison across locations. This study developed an exposure-normalized framework to model incident intensity at the county level using accumulated incidents per crossing (AIPC), which normalizes cumulative incidents by crossing exposure. The analysis integrated statistical distribution modeling, spatial clustering, and supervised machine learning. The study combined county-level HRGC data for the contiguous United States from 1975 to 2025 with infrastructure, traffic, environmental, and accessibility variables. Results showed that AIPC was consistent with a gamma distribution, indicating a continuous representation of incident intensity without discrete risk regimes. Local Moran’s I identified statistically significant high-intensity clusters in specific regions, confirming spatial dependence in incident intensity. Machine learning models achieved strong predictive performance, with the extra trees model reaching AUC = 0.907 (F1 = 0.528) and ensemble methods consistently outperforming linear and kernel approaches. SHAP and permutation-based feature importance analysis identified temperature, train frequency, and accessibility measures as the most influential predictors, while aggregate density measures contributed the least. The results provided consistent evidence that incident intensity was associated with environmental conditions, operational exposure, and network structure. The proposed framework supports exposure-based risk assessment and enables identification of high-intensity counties for targeted intervention. This approach provides a transparent and transferable method for improving HRGC safety analysis and prioritizing resource allocation across large geographic areas. Full article

As livestock production continues to consolidate into fewer but larger operations, odor complaints from neighboring communities have become a major challenge to industry growth, making the establishment of appropriate odor setback distances essential. This paper reiterates the development procedure of odor footprint tools for setback determination based on AERMOD, a regulatory air dispersion model, using North Dakota as an example. Specifically, we developed North Dakota Odor Footprint Tool (NDOFT), an Excel-based calculator designed to estimate odor setback distances between animal production facilities and surrounding communities. The tool utilizes county-specific meteorological data to predict odor concentrations at various distances and directions relative to an established annoyance threshold of 75 OU m⁻³. Setback distances are determined based on the percentage of time during which modeled odor concentrations remain below this threshold, corresponding to annoyance-free frequencies ranging from 91% to 99%. Facility characteristics, including livestock types, source areas, and odor control measures, are incorporated to enable scenario-based assessments. The influence of complex terrain on setback determination was also evaluated, revealing that no simple correction factors adequately capture terrain effects for valleys and hilltops. Overall, the use of county-specific meteorological inputs substantially improves the accuracy of predicted setback distances compared with area-representative approaches, providing an updated and more robust framework for odor setback planning and environmental evaluation. This work is expected to guide future efforts in developing and refining odor setback tools. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article