Search Results (59)

While global nutrient insufficiency remains a critical health challenge, eggs have emerged as a potential solution due to their profile as an accessible and nutrient-dense food source. To quantitatively assess this potential for mitigating nutrient insufficiencies and guide the production of nutrient-enriched eggs, the study proposes the concept of egg nutriomics, establishing a comprehensive evaluation system with 35 indicators across seven nutritional dimensions (fatty acids, amino acids, vitamins, trace elements, pigments, antioxidant capacity, and dietary restriction factors). Methodologically, the system normalizes raw analytical data into standardized scores (0–100) using indicator-specific functional models, with weights rationally allocated based on the essentiality of the nutrients. These quantitative metrics are subsequently translated into intuitive results using visualization tools such as heatmaps and radar charts. This study applied this system to evaluate six commercial egg varieties (pasteurized, lutein-enriched, ω-3 enriched, animal welfare, low-cholesterol, and conventional cage eggs), profiling multidimensional nutrition that allows for the intuitive visualization of performance scores across distinct dimensions. These profiles extend beyond comprehensive evaluation by revealing specific quantitative advantages—such as ω-3 enriched eggs scoring 79 in the fatty acid dimension compared to 49 for conventional eggs—thus providing a reference to guide precision modulation as illustrated by a dietary ω-3 enrichment case study involving 200 laying hens. Building upon this foundation, the strategy empowers a shift from the sole pursuit of high yields to precision nutritional modulation. This multi-dimensional strategy bridges nutritional analysis with production control, facilitating the development of nutrient-dense eggs as a potential application to mitigate human malnutrition. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

This study benchmarks two nanocellulose (NC) production architectures: sulfuric-acid hydrolysis of pineapple peel biomass to obtain hydrolyzed nanocellulose (HNC) and microbial biosynthesis of bacterial nanocellulose (BNC) by Rhizobium leguminosarum biovar trifolii in defined media. HNC and BNC were characterized by SEM, FTIR, AFM, and ζ-potential, and the routes were compared using a sustainability-focused multicriteria framework. The Visual Integration of Multicriteria Evaluation (VIME) (radar chart + weighted decision matrix) yielded a higher overall score for BNC (66) than HNC (51), driven primarily by lower downstream washing/neutralization water demand (~0.3 L vs. ~14 L per batch), fewer purification stages (~2 vs. ~5), and lower waste hazard. In contrast, HNC performed better in calendar time (~7 vs. ~18 days). AFM revealed route-dependent morphologies: BNC formed a homogeneous nanofiber network (37 ± 9 nm), while HNC formed heterogeneous lamellar fragments (70 ± 12 nm). Route-specific yields were 3.15% (w/w, dry biomass basis) for HNC and 1.065 g/L (culture-volume basis) for BNC. Although a full ISO-compliant Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) are beyond the scope of this laboratory-scale study, the defined system boundaries and reported process inventories provide an LCA/TEA-ready template for future mass- and cost-balanced comparisons. Full article

Recent studies have reported a rapid increase in short-duration, high-intensity rainfall over the Seoul Metropolitan Area (SMA), primarily associated with mesoscale convective systems (MCSs), highlighting the need for high-resolution and multi-platform observations for accurate forecasting. To address this challenge, the Korea Meteorological Administration (KMA) established the Korean Precipitation Observation Program (KPOP), an intensive observation network integrating radar, wind lidar, wind profiler, and storm tracker measurements. This study introduces the design and implementation of the KPOP network and evaluates its observational and forecasting value through a heavy rainfall event that occurred on 17 July 2024. Wind lidar data and weather charts reveal that a strong low-level southwesterly jet and enhanced moisture transport from the Yellow Sea played a key role in sustaining a quasi-stationary, line-shaped rainband over the metropolitan region, leading to extreme short-duration rainfall exceeding 100 mm h⁻¹. To investigate the impact of KPOP observations on numerical prediction, preliminary data assimilation experiments were conducted using the Korean Integrated Model-Regional Data Assimilation and Prediction System (KIM-RDAPS) with WRF-3DVAR. The results demonstrate that assimilating wind lidar observations most effectively improved the representation of low-level moisture convergence and spatial structure of the rainband, leading to more accurate simulation of rainfall intensity and timing compared to experiments assimilating storm tracker data alone. These findings confirm that intensive, high-resolution wind observations are critical for improving initial analyses and enhancing the predictability of extreme rainfall events in densely urbanized regions such as the SMA. Full article

To address the high-carbon emissions associated with the large use of Portland cement (PC) in traditional engineered cementitious composites (ECCs) and the resource constraints on supplementary cementitious materials (SCMs), this study proposes a strategy combining limestone calcined clay cement (LC³) as a PC replacement with the incorporation of hybrid synthetic fibers to develop low-carbon, environmentally friendly ECCs. The fundamental properties of the LC³-ECC were tested, and a sustainability analysis was conducted. The experimental results show that an increase in water-to-binder ratio (W/B) or superplasticizer (SP) dosage significantly enhanced fluidity while reducing the yield stress and plastic viscosity. An LC³-ECC with a W/B of 0.25, 0.45% SP and 2% polyethylene fibers exhibited the best tensile performance, achieving an ultimate tensile strain of 8.40%. In contrast, an increase in polypropylene fiber led to a degradation in crack-resistant properties. In terms of sustainability, replacing the PC with LC³ significantly reduced carbon emissions by 19.1–20.8%, while the cost of the limestone calcined clay cement–polypropylene fiber (LC³-PP) was approximately 50% of that of the limestone calcined clay cement–polyvinyl alcohol fiber (LC³-PVA). Furthermore, an integrated evaluation framework encompassing rheological, mechanical and environmental factors was established using performance radar charts. The dataset on the performance results and the developed assessment framework provide a foundation for optimizing the mixture proportioning of LC³-ECC in practical engineering applications. Full article

Dewatering of sewage sludge is a key operational element of wastewater treatment plants and has major economic implications, as it entails the costs of thickening, transport, and disposal. The aim of this study was to determine the influence of selected polyelectrolytes and their dosages on dewatering efficiency and to present an innovative, multicriteria method of result evaluation using radar charts. In this research, 10 different polyelectrolytes were assessed in terms of sludge dewaterability, considering conditioning parameters including Specific Resistance to Filtration (SRF), Capillary Suction Time (CST), and centrifugation performance. The results were presented in the form of radar charts, enabling both an overall evaluation of the effectiveness of each product and an assessment of their suitability for specific dewatering technologies, such as belt filter presses and centrifuges. The analysis showed that polyelectrolytes with higher cationic charge provided better dewatering performance. The proposed visualization method allows us to analyze the effects across different conditioners and technologies. The best sludge conditioning effect (maximum radar chart area) was achieved with Praestol 665, a polyelectrolyte with a high cationic charge level. This method is a practical tool for selecting the optimal agent for sewage sludge dewatering. Full article

High spatial resolution sea ice concentration (SIC) is crucial for global climate and marine activity. However, retrieving high spatial resolution SIC from passive microwave sensors is challenging due to the trade-off between spatial resolution and atmospheric contamination. Our study develops the Ice-WaterNet framework, a novel superpixel-based deep learning model that integrates Conditional Random Fields (CRF) with a dual-attention U-Net to enhance ice–water classification in Synthetic Aperture Radar (SAR) imagery. The Ice-WaterNet model has been extensively tested on 2735 Sentinel-1 dual-polarized SAR images from 2021 to 2023, covering both winter and summer seasons in the Fram Strait. To tackle the complex surface features during the melt season, wind-roughened open water, and varying ice floe sizes, a superpixel strategy is employed to efficiently reduce classification uncertainty. Uncertain superpixels identified by CRF are iteratively refined using the U-Net attention mechanism. Experimental results demonstrate that Ice-WaterNet achieves significant improvements in classification accuracy, outperforming CRF and U-Net by 3.375% in Intersection over Union (IoU) and 3.09% in F1-score during the melt season, and by 1.96 in IoU and 1.75 in F1-score during the freeze season. The derived high-resolution SIC products, updated every two days, were evaluated against Met Norway ice charts and compared with ASI from AMSR-2 and SSM/I, showing a substantial reduction in misclassification in marginal ice zones, particularly under melting conditions. These findings underscore the potential of Ice-WaterNet in supporting precise sea ice monitoring and climate change research. Full article

Nanopriming with metal nanoparticles (NPs) is a promising strategy for improving seedling quality in horticultural crops. This study evaluated the effects of hydropriming, ZnO, SiO₂, ZnO + SiO₂, a ZnMo nanofertilizer, and two commercial biostimulants (Osmoplant and Codasil) on the early development of Capsicum annuum L. seedlings. Morphological, physiological, and biochemical traits, including biomass, stem architecture, number of leaves, chlorophylls, carotenoids, SPAD index, and nitrate reductase (NR) activity, were measured under controlled conditions. The ZnO and ZnO + SiO₂ treatments promoted stronger root growth, higher pigment content, and higher NR activity. SiO₂ alone and ZnMo showed intermediate improvements, while Osmoplant and Codasil had more limited effects. Multivariate analyses provided complementary information: heat maps revealed correlations between traits, PCA differentiated treatment responses, and radar charts integrated performance profiles. Overall, the results provide promising evidence that seed nanopriming, particularly with ZnO and ZnO + SiO₂, improves seedling vigor and transplant potential in jalapeño peppers. Full article

With the increasing air traffic flow, the workload of air traffic controllers is also growing, and their proficiency directly impacts civil aviation safety and efficiency. To address the lack of clear training objectives and inconsistent evaluation methods in the initial controller training at the Southwest Air Traffic Management Bureau, this study aimed to develop and validate a core competency model for initial air traffic controllers. Referencing ICAO Document 10056, the study first defined core competencies. Subsequently, using job analysis, the behavioral event interview (BEI) method, and expert panels, a core competency model tailored to the training objectives of the Southwest ATMB was constructed. The key findings of this research include: first, the defined structure of the developed model, comprising seven competency dimensions, 21 elements, and 26 observable behaviors (OBs); second, the determination of combined weights for each dimension and indicator using questionnaire surveys, the Analytic Hierarchy Process (AHP), and the Entropy Weight Method; and third, the successful application and validation of the model. Specifically, in its application, the weighted TOPSIS method was employed to evaluate trainees in a specific group. This not only provided a ranking of trainee abilities but also facilitated in-depth analysis through radar charts of competency dimensions and box plots of OB items. These application results demonstrate the model’s effectiveness and practicality. Full article

Crops in greenhouses located in cold climates are frequently affected by high relative humidity (RH). This study presents the design, testing, and analysis of a dehumidifier based on thermoelectric cooling. Thermoelectric dehumidifiers (TEDs) are capable of dehumidifying greenhouses in cold regions while recovering heat for indoor air heating. The design of a TED is based on the specific characteristics of thermoelectric coolers (TECs). A TED consists of a cabinet, four heat exchangers, a duct fan, a water pump, and auxiliary components. The TED performance was evaluated in a Chinese solar greenhouse (CSG) with a volume of approximately 160 m³. The input voltage of the TECs, fan airflow rate, and cold-side fin area affected the TED performance, with their influence varying in magnitude. The radar chart results show that the optimal operating parameters are as follows: a fan airflow rate of 300 m³/h, a TEC input voltage of 15 V, and a cold-side fin area of 0.15 m². With the TED running for 120 min under the optimal parameters, the RH in the CSG decreased by 25.5%, while the air temperature increased by 3.4 °C. The installation of the TED at the bottom of the CSG improved the growing environment of the crops, particularly in the vertical range between 0.2 m and 1.5 m height inside the greenhouse. These findings provide a valuable reference for applying thermoelectric cooling technology in the greenhouse field. Full article

Using coal gangue in highway base construction provides a sustainable and high-value solid waste recycling approach. This research focused on the mechanical and durability properties of coal gangue from tunneling operations. Six experimental tests, such as unconfined compressive strength (UCS), flexural–tensile strength (FTS), etc., were carried out. The impact of aggregate gradation on coal gangue mixtures’ performance was systematically evaluated. XRD and SEM were used to explore the microstructural mechanisms in cement-stabilized coal gangue–gravel mixtures (CGM). An improved evaluation model, the Delphi–entropy weight–TOPSIS (DET) method, integrating Delphi and entropy weighting, was proposed. Together with an advanced radar chart, it evaluates eight performance criteria, including mechanical, durability, economic, and environmental aspects. The results show that increasing the coal gangue content in mixtures decreases UCS, dynamic compressive rebound modulus (DCRM), FTS, fatigue life, and drying shrinkage performance. Coarse aggregates relieve drying shrinkage, while fine ones improve long-term mechanical properties. Gradation T1~3 promotes the formation of C–S–H gel and AFt crystals, enhancing compactness. Based on the DET model’s quantitative evaluation, T1~3 was determined as the optimal mix for expressway bases, achieving a balance between mechanical performance, durability, and sustainability. Full article

In light of the challenges confronting urban areas due to increasing populations and spatial constraints, urban green infrastructure is vital for fostering environmental balance, enhancing community well being, and promoting sustainable urban development. This situation underscores the necessity for strategies that reconcile the escalating demand for constructed environments with the enhancement of urban green infrastructure in urban areas. This study seeks to empirically investigate an integrated spatial analysis approach that synthesises the quality of urban green infrastructure and land characteristics by incorporating diverse perspectives, utilising the Altstetten-Albisrieden district of Zurich as a case study. It systematically evaluates factors including development density, green surface coverage, leaf area, green ratio and connectivity, and the accessibility of public green spaces within the studied district. A 10-m rectangular grid was employed to visualise and integrate the evaluation results from different perspectives. Furthermore, clustering algorithms were utilised to generate spatial patterns indicative of unique land characteristics. By comparing the results from various clustering algorithms, this study adopted the fifteen clusters derived from the K-Means method, employing radar charts to describe the characteristics of each cluster, and partitioned the district into five zones to provide recommendations regarding the provision and optimisation of urban green infrastructure within the district. Ultimately, it highlighted the necessity of increasing community gardens and green spaces in densely built areas and leveraging existing structures to augment vegetation and plant life for the enhancement of ecological benefits. Full article

This study establishes a comprehensive framework for evaluating sustainability by integrating radar chart analysis with fuzzy linguistic methods, helping enterprises select suppliers that align with sustainable development principles. Testing within the textile industry confirmed the framework’s reliability, effectively identifying strengths and areas for improvement across key sustainability dimensions: environmental, social, economic, and governance. The analysis highlighted strong supplier performance in governance and social responsibility, particularly in human rights protection, while emphasizing the need for improvements in environmental aspects such as water resource management and product recycling. Economic factors, including product quality and delivery capability, demonstrated significant potential, though cost efficiency remained a challenge. Recommendations focus on enhancing board diversity, employee satisfaction, and circular economy initiatives. The framework demonstrates strong applicability within the textile industry and has the potential for adaptation in other sectors. Future research should conduct industry-specific validations to refine weight allocation for dynamic markets. Full article

The interior of a pigsty is a nonlinear system formed by multiple interacting environmental factors, making it challenging to reasonably and accurately assess the environmental comfort levels. To address this, we propose an environmental comfort evaluation index based on livestock farming standards. By combining the analytic hierarchy process (AHP) and entropy weight method (EWM), we determine the weights of each evaluation index. Finally, the evaluation results are visualized using radar charts, and the model is validated. We apply this model to monitor and analyze environmental factors in a fattening pigsty at a farm in Central Jilin Province. The results demonstrate that the AHP-EWM multi-factor comprehensive evaluation method effectively reflects overall environmental comfort variations in the pigsty and captures interactions among environmental factors across different time periods. This study establishes a methodological foundation for comprehensive pigsty environmental assessment, precision control, and enhanced environmental comfort. Full article

Wuyi Rock tea (WRT), originating from the northern region of Fujian province, has a good reputation for its distinctive Yan flavor and floral–fruity aroma. The aroma quality, an essential element of the Yan flavor, undergoes various changes during the manufacturing process of WRT. To enhance the understanding of the formation patterns of WRT aroma and its influence on the flavor quality of WRT, we utilized both manufactured WRT (Rougui tea) and primary tea as materials. Utilizing a sensory evaluation, detection of volatile compounds, and multivariate statistical analysis, we identified and characterized the distinctive volatile components present in WRT. The sensory evaluation and radar chart analysis revealed that the primary tea exhibited a strong and lasting aroma, along with a mellow taste and a prominent Yan flavor. Through gas chromatography time-of-flight mass spectrometry (GC-TOF MS), a total of 251 volatile compounds were identified. The odor activity value (OAV) was calculated to identify key aroma-active compounds in the primary tea. The results indicated that a total of 14 compounds had an OAV greater than 1.0, including (2-nitroethyl) benzene, indole, and geranylacetone. These compounds exhibited floral and fruity aroma attributes. They primarily formed and accumulated during the latter stages of WRT. Using a partial least squares discrimination analysis (PLS-DA) combined with a variable importance in projection (VIP) score greater than 1.0 as a criterion, a total of 89 compounds were identified. Furthermore, out of the selected compounds, 15 types, including geraniol, 1-nonanol, and 1-butyl-2-ethyl-cyclopropene, were found to exclusively exist during the enzymatic manufacturing stages, particularly during the intermediate and later phases of the turn-over process (the last-three-times turn-over treatments), exhibiting predominantly floral and sweet fragrances. In contrast, during the non-enzymatic stages, only four compounds, such as pentanoic acid and phenylmethyl ester, were detected, exhibiting a fruity aroma profile. These volatile compounds significantly influenced the quality attributes of the final tea product, resulting in strong and lasting characteristics, particularly marked by a pronounced floral and fruity aroma. This study revealed how the aroma quality in WRT is developed and pinpointed possible volatile compounds that react to post-harvest treatments, thereby offering valuable insights relating to the intelligent production strategies of WRT. Full article