Search Results (50,659)

This article presents the results of a systematic review investigating the potential of agricultural wastes as sustainable and low-carbon alternatives in reinforced concrete (RC) production. Background: The depletion of natural resources and the environmental burden of conventional construction materials necessitate innovative solutions to reduce the carbon footprint of construction. Agricultural wastes, including coconut shells (CSs), rice husk ash (RHA), and palm oil (PO) fuel ash, emerge as promising materials due to their abundance and mechanical benefits. Objective: This review evaluates the potential of agricultural wastes to improve sustainability and enhance the mechanical properties of RC structural elements while reducing carbon emissions. Design: Studies were systematically analyzed to explore the sources, classification, and material properties of agro-wastes (AWs), with a particular focus on their environmental benefits and performance in concrete. Results: Key findings demonstrate that AWs enhance compressive strength, tensile strength, and modulus of elasticity while reducing the carbon footprint of construction. However, challenges such as variability in material properties, limited long-term durability data, and lack of standardized guidelines hinder their broader adoption. Conclusions: AWs hold significant potential as sustainable additives for RC elements, aligning with global sustainability goals. Future research should address material optimization, lifecycle assessments, and regulatory integration to facilitate their mainstream adoption in construction. Full article

The issues related to soil environmental contamination caused by heavy metals have garnered increasing attention. In particular, the soil pollution risk in the eastern coastal regions of China has attracted widespread concern. This study surveyed heavy metals in the soils near the Jiahe River Basin of Yantai City in Shandong Province, China. A total of 213 soils were sampled and analyzed for 12 items: Cr, Hg, As, Pb, Cd, Cu, Ni, Zn, Co, V, Mn, and pH. The 11 heavy metals were evaluated using the national standard GB15618-2018, with three risk levels of background, screening, and intervention, and using pollution indices, including the contamination factor (C_f), ecological risk factor (Er), enrichment factor (EF), and index of geo-accumulation (I_geo), with different respective risk levels. The results indicate a strong consistency between the evaluations both for the index I_geo and for GB15618-2018 on five metals (i.e., Cr, Hg, As, Pb, and Cd). Therefore, the index I_geo may serve as a supplementary indicator for assessing the pollution risks of heavy metals in agricultural soils regarding samples of Cu, Ni, and Zn that exceed the screening values in GB15618-2018, as well as for Co, V, and Mn, which have not yet been established in GB15618-2018. According to the three-level classification of risk in GB15618-2018, the seven commonly used levels of the index I_geo are also incorporated into the three levels of background, screening, and intervention. The overall pollution risk of 11 heavy metals in the soils of the Jiahe River Basin of Yantai City belongs to the background level. Specifically, Hg and Pb in the total area are classified at the background level. Manganese, V, Co, Zn, Ni, and Cr are recognized at the screening level sporadically, while Cu, As, and Cd are found at the screening level in small areas. No areas within the region are classified at the intervention level. Full article

This study investigates how different types of incentives impact the performance of rail transit PPPs, focusing on their construction and operational phases. By surveying 121 practitioners working in the Chinese rail transit industry, we propose a new classification of incentives (i.e., control-oriented, neutrality-oriented, and recognition-oriented incentives) based on psychological theories to broaden the categorization of “positive” (rewards) and “negative” (punishment) incentives. We further explore how these multi-dimensional incentives influence project performance by surveying another 256 industry professionals. Our findings reveal that (1) in addition to punishments, performance-based payment/bonus, credit ratings, and reputation mechanisms are newly recognized as control-oriented incentives, which can restrain the autonomy of the private sector; (2) control-oriented incentives positively influence project performance in the construction phase where clear, measurable goals are available, but their impact diminishes in the operational phase; (3) recognition-oriented incentives enhance project performance in both construction and operational phases (especially the latter), fostering long-term sustainability; and (4) neutrality-oriented incentives focus on risk allocation and collaboration between public and private sectors, showing a modestly positive effect in the operational phase. As such, the study provides decision-makers in the rail transit industry with valuable insights to enhance project performance effectively when implementing incentive policies. Full article

Optical burst switching (OBS) is a network architecture that combines the advantages of packet and circuit switching techniques. However, OBS networks are susceptible to cyber-attacks, such as flooding attacks, which can degrade their performance and security. This paper introduces a novel machine learning method for flooding attack detection in OBS networks, based on a third-order distance function for k-nearest neighbors (KNN3O). The proposed distance is expected to improve detection accuracy due to higher sensitivity with respect to the distance difference between two points. The developed method is compared with seven other machine learning methods, namely standard KNN, KNN with cosine distance (KNNC), multi-layer perceptron (MLP), naive Bayes classifier (NBC), support vector machine (SVM), decision tree (DT), and discriminant analysis classifier (DAC). The methods are further assessed using five metrics: accuracy, precision, recall, F1-score, and specificity. The proposed method achieved an accuracy of 99.3%, outperforming the original KNN, MLP, and SVM, which achieved accuracies of 99%, 76.4%, and 94.7%, respectively. The results show that KNN3O is the best method for flooding attack detection in OBS networks, as it achieves the highest scores in all five metrics. Full article

In agricultural production, droughts occurring during the crucial growth periods of crops hinder crop development, while the daily-scale standardized precipitation evapotranspiration index (SPEI) can be applied to accurately identify the drought characteristics. In this study, we used the statistical downscaling method to obtain the daily precipitation (P_r), maximum air temperature (T_max) and minimum air temperature (T_min) during the rice growing season in Heilongjiang Province from 2015 to 2100 under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 in CMIP6, to study the spatial and temporal characteristics of drought during the rice growing season in cold region and the effect of climate change on drought characteristics. The potential evapotranspiration (PET₀) was calculated using the regression correction method of the Hargreaves formula recommended by the FAO, and the daily SPEI was calculated to quantitatively identify the drought classification. The Pearson correlation coefficient was used to analyze the correlation between the meteorological factors (P_r, T_max, T_min), PET₀ and SPEI. The results showed that: (1) Under 3 SSP scenarios, P_r showed an increasing trend from the northwest to the southeast, T_max showed an increasing trend from the northeast to the southwest, and higher T_min was mainly distributed in the east and west regions. (2) PET₀ indicated an overall interannual rise in the three future SSP scenarios, with higher values mainly distributed in the central and western regions. The mean daily PET₀ values ranged from 4.8 to 6.0 mm/d. (3) Under SSP1-2.6, rice mainly experienced mild drought and moderate drought (−0.5 ≥ SPEI > −1.5). The predominant drought classifications experienced were mild, moderate, and severe drought under SSP2-4.5 and SSP8.5 (−0.5 ≥ SPEI > −2.0). (4) The tillering stage experienced the highest drought frequency and drought intensity, with the longest drought lasting 24 days. However, the heading flower stage had the lowest drought frequency and drought intensity. The drought barycenter was mainly in Tieli and Suihua. (5) The PET₀ was most affected by the T_max, while the SPEI was most affected by the P_r. This study offers a scientific and rational foundation for understanding the drought sensitivity of rice in Northeast China, as well as a rationale for the optimal scheduling of water resources in agriculture in the future. Full article

Deep learning models, known as convolutional neural networks (CNNs), have paved the way for reliable automated image recognition. These models are increasingly being applied in research on freshwater biodiversity, aiming to enhance efficiency and taxonomic resolution in biomonitoring. However, insufficient or imbalanced datasets remain a significant bottleneck for creating high-precision classifiers. The highly imbalanced data, where some species are rare and others are common, are typical of the composition of most benthic communities. In this study, a series of CNN models was built using 33 species of aquatic insects, with datasets ranging from 10 to 80 individuals, to determine the optimal number of individuals each class should have to build a high-precision classifier. We also consider the effect of class imbalance in the training dataset and the use of oversampling technique. The results showed that a robust model with acceptable accuracy (99.45%) was achieved with at least 30 individuals per class. A strongly imbalanced dataset caused an approximately 2% decrease in classification accuracy, while a moderately imbalanced dataset had no significant effect. The application of the oversampling technique enhanced in 1.88% the accuracy of strongly imbalanced models. These findings can help effectively tailor future aquatic macroinvertebrate training datasets. Full article

Background: Wound healing is a complex physiological process that begins immediately upon injury. Nutritional status significantly affects the course of regenerative processes. Malnutrition can prolong the inflammatory phase, limit collagen synthesis, and increase the risk of new wound formation. The issue of malnutrition is becoming increasingly prevalent and remains a significant concern, particularly among older adults dealing with chronic conditions. Methods: The study was conducted at the Wound Treatment Clinic of the Specialist Hospital at the Podkarpackie Oncology Center in Brzozów, Poland, over 12 months (31 December 2022 to 31 December 2023). A prospective assessment was carried out on 106 patients with chronic wounds. The sample selection was purposeful, based on the following criteria: individuals with hard-to-heal vascular wounds related to diabetic foot disease or venous insufficiency, who provided informed consent to participate after reviewing the study concept. The assessment included a questionnaire and biochemical blood analysis. Further evaluations covered wound characteristics and classification based on clinical scales. The morphotic and biochemical blood parameter assessment included albumin concentration, hemoglobin, C-reactive protein (CRP), and the nutritional risk index (NRI). Results: A larger wound area was associated with lower morphotic values in both groups. Exudate levels and severity in chronic venous insufficiency (CVI) patients and diabetic foot disease (DFD) were associated with lower hemoglobin, albumin, and NRI values. At the same time, the depth of tissue structure damage correlated with the measured biochemical parameters. Conclusions: NRI values and morphotic blood parameters, along with albumin, hemoglobin, and CRP levels, are closely associated with wound characteristics, including surface area, exudate level, and the severity of tissue destruction. The greater the destruction of tissue structures, the higher the risk of malnutrition and wound infection, as indicated by biochemical assessment. Full article

The basis for the safe extraction of mineral resources underground is good knowledge of the local and surrounding geological conditions and the activity of the nearby aquifers. Hydrogeological modelling in combination with dewatering of the aquifers above the coal and monitoring of the groundwater level in piezometers is of particular importance for safe underground coal mining in the Velenje mine. This study shows the contribution of an improved hydrogeological conceptual model to the prediction of groundwater movement in the aquifers above the coal seam using a hydrodynamic six-layer model. The improved hydrogeological conceptual model is based on the determination of the groundwater age and a detailed geological classification of the layers. The groundwater ages, determined using the tritium detection method, were important to understand the recharge of the individual aquifers. As there is no direct recharge at the surface, the aquifers are only recharged by the slow leakage of groundwater from the upper to the lower aquifers. The hydrodynamic six-layer model, which is based on an improved hydrogeological conceptual model, now simulates groundwater more accurately than previous hydrodynamic models and helps with dewatering planning and the technical design of mining facilities near aquifers. Full article

We conduct a literature review on integrated land use modelling to guide policy on sustainable food provisioning. Modelling approaches are discussed in the spatial, temporal, and human dimensions, as well as environmental and socio-economic considerations. Many studies have focused on model development over their application to specific policy objectives, often relying on top-down approaches. While ecosystem services are a frequent focus, indicators for their assessment are inconsistently quantified. Socio-economic considerations are coarse in scale compared to biophysical ones, limiting their use in nuanced policy development. Recommendations are made such as standardising data collection and sharing to streamline modelling processes and collaboration. Comprehensive ecosystem services frameworks would benefit from a more uniform classification of values. More bottom-up modelling, connected with top-down models, could account for the heterogeneity between smaller units of analysis such as the landscape, farms, or people. This could reveal further insights into the local contexts and decision-making responses essential for effective land use policy. These advancements would help to design policies that address the complexities of sustainable food provisioning. By connecting local and global perspectives, future models can support more resilient and adaptive food systems, ensuring sustainability in the face of environmental and socio-economic challenges. Full article

Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. Results: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. Conclusions: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis. Full article

Deep neural network-based synthetic aperture radar (SAR) automatic target recognition (ATR) systems are susceptible to attack by adversarial examples, which leads to misclassification by the SAR ATR system, resulting in theoretical model robustness problems and security problems in practice. Inspired by optical images, current SAR ATR adversarial example generation is performed in the image domain. However, the imaging principle of SAR images is based on the imaging of the echo signals interacting between the SAR and objects. Generating adversarial examples only in the image domain cannot change the physical world to achieve adversarial attacks. To solve these problems, this article proposes a framework for generating SAR adversarial examples in a 3D physical scene. First, adversarial attacks are implemented in the 2D image space, and the perturbation in the image space is converted into simulated rays that constitute SAR images through backpropagation optimization methods. The mapping between the simulated rays constituting SAR images and the 3D model is established through coordinate transformation, and point correspondence to triangular faces and intensity values to texture parameters are established. Thus, the simulated rays constituting SAR images are mapped to the 3D model, and the perturbation in the 2D image space is converted back to the 3D physical space to obtain the position and intensity of the perturbation in the 3D physical space, thereby achieving physical adversarial attacks. The experimental results show that our attack method can effectively perform SAR adversarial attacks in the physical world. In the digital world, we achieved an average fooling rate of up to 99.02% for three objects in six classification networks. In the physical world, we achieved an average fooling rate of up to 97.87% for these objects, with a certain degree of transferability across the six different network architectures. To the best of our knowledge, this is the first work to implement physical attacks in a full physical simulation condition. Our research establishes a theoretical foundation for the future concealment of SAR targets in practical settings and offers valuable insights for enhancing the attack and defense capabilities of subsequent DNNs in SAR ATR systems. Full article

Background/Objectives: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists. This study evaluates and compares the performance of four CNN architectures—DenseNet121, ResNet50V2, NASNetMobile, and MobileNetV2—for the binary classification of dermoscopic images. Methods: A dataset of 8825 dermoscopic images from DermNet was standardized and divided into training (80%), validation (10%), and testing (10%) subsets. Image augmentation techniques were applied to enhance model generalizability. The CNN architectures were pre-trained on ImageNet and customized for binary classification. Models were trained using the Adam optimizer and evaluated based on accuracy, area under the receiver operating characteristic curve (AUC-ROC), inference time, and model size. The statistical significance of the differences was assessed using McNemar’s test. Results: DenseNet121 achieved the highest accuracy (92.30%) and an AUC of 0.951, while ResNet50V2 recorded the highest AUC (0.957). MobileNetV2 combined efficiency with competitive performance, achieving a 92.19% accuracy, the smallest model size (9.89 MB), and the fastest inference time (23.46 ms). NASNetMobile, despite its compact size, had a slower inference time (108.67 ms), and slightly lower accuracy (90.94%). Performance differences among the models were statistically significant (p < 0.0001). Conclusions: DenseNet121 demonstrated a superior diagnostic performance, while MobileNetV2 provided the most efficient solution for deployment in resource-constrained settings. The CNNs show substantial potential for improving melanoma detection in clinical and mobile applications. Full article

Background/Objectives: This international multicenter study aimed to assess the effectiveness of the Pivot-Shift Meter (PSM) mobile application in diagnosing and classifying anterior cruciate ligament (ACL) injuries, emphasizing the need for standardization to improve diagnostic precision and treatment outcomes. Methods: ACL evaluations were conducted by eight experienced orthopedic surgeons across five Latin American countries (Bolivia, Chile, Colombia, Ecuador, and Mexico). The PSM app utilized smartphone gyroscopes and accelerometers to standardize the pivot-shift test. Data analysis from 224 control tests and 399 standardized tests included non-parametric statistical methods, such as the Mann–Whitney U test for group comparisons and chi-square tests for categorical associations, alongside neural network modeling for injury grade classification. Results: Statistical analysis demonstrated significant differences between standardized and control tests, confirming the effectiveness of the standardization. The neural network model achieved high classification accuracy (94.7%), with precision, recall, and F1 scores exceeding 90%. Receiver Operating Characteristic (ROC) analysis yielded an area under the curve of 0.80, indicating reliable diagnostic accuracy. Conclusions: The PSM mobile application, combined with standardized pivot-shift techniques, is a reliable tool for diagnosing and classifying ACL injuries. Its high performance in predicting injury grades makes it a valuable addition to clinical practice for enhancing diagnostic precision and informing treatment planning. Full article

The early prediction of Alzheimer’s disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time–frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via t-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time–Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research. Full article

To protect aquatic organisms in ecosystems, each country and continental union has established guidelines for deriving the water quality standards (WQS) of specific substances. These guidelines mandate the use of acute and chronic toxicity data for fish, which are high-trophic-level organisms. However, due to increasing concerns about animal welfare and experimental ethics, there is a growing need for alternative methods to determine substance toxicity in fish. Fish toxicity tests using early life stages, such as embryos or larvae, have been utilized as alternative methods for adult fish toxicity assays. This review of the WQS guidelines and relevant test protocols confirmed the classification of acute and chronic toxicity in fish assays using different developmental stages. Fish toxicity data derived from exposure periods longer than one week using embryonic- or larval-stage organisms can be considered as indicative of chronic toxicity. There is a high correlation between fish embryo toxicity and adult effects, suggesting that fish embryo toxicity tests with appropriate exposure durations could replace adult fish toxicity tests, addressing experimental animal ethics concerns. Full article