Search Results (3,383)

Due to its benefits and efficiency, mechanized sugarcane harvest is a common practice in Brazil; however, continuous traffic of agricultural machinery leads to soil compaction at the end of each harvest cycle. Hence, this study evaluated whether machine traffic affects soil physical and hydraulic properties, root growth, and crop productivity in sugarcane areas during different harvest cycles. Four treatments were performed consisting of an area planted with different stages (years) of sugarcane crop: T1 = after the first harvest—plant cane (area 1); T2 = after the second harvest—first ratoon cane (area 2); T3 = after the third harvest—second ratoon cane (area 3); T4 = after fourth harvest—third ratoon cane (area 4). Five sampling sites were considered in each area, constituting five replicates collected from four layers. Two collection positions were considered: wheel track (WT) and planting row (PR). Soil physical properties, root system, productivity, and biometric characteristics of the sugarcane crop were evaluated at depths of 0.00–0.05 m, 0.05–0.10 m, 0.10–0.20 m, and 0.20–0.40 m. Traffic during the sugarcane crop growth cycles affected soil physical and hydraulic properties, showing sensitivity to the effects of the different treatments, producing variations in root growth and crop productivity. Plant cane cycle showed lower soil penetration resistance, bulk density, microporosity, higher saturated soil hydraulic conductivity, and macroporosity when compared with the other cycles studied. In the 0.10–0.20 m layer, all treatments produced higher soil penetration resistance and density, and lower saturated soil hydraulic conductivity. Dry biomass, volume, and root area were higher for the plant cane cycle in the 0.00–0.05 m and 0.05–0.10 m layers compared with the other crop cycles. Root dry biomass is directly related to crop productivity in layers up to 0.40 m deep. Sugarcane productivity was affected along the crop cycles, with higher productivity observed in the plant cane and first ratoon cane cycles compared with the second and third ratoon cane cycles. Full article

The estimated ultimate recovery (EUR) is an important parameter for forecasting oil and gas production and informing decisions regarding field development strategies. In this study, we combined site-specific geologic, completion, and operational parameters with the predictive capabilities of machine learning (ML) models to predict EURs of the wells for the Eagle Ford Marl Continuous Oil Assessment Unit. We developed an extensive dataset of wells that have produced from the lower and upper Eagle Ford Shale intervals and reduced the model complexity using principal component analysis. We tested the ML models and estimated the sensitivities of ML-predicted EURs to changes in the values of different input variables. The results of applying the optimized ML model to the Eagle Ford suggest that the approach developed in this study could be promising. The ML estimates of the EURs fit the DCA-based values with an R² ~ 0.9 and a mean absolute error of ~36 × 10³ bbl. In the lower Eagle Ford Shale, the EUR estimates were found to be most sensitive to changes in porosity, net thickness of the interval, clay volume, and the API gravity of the oil; and that in the upper Eagle Ford Shale they were most sensitive to changes in the total organic carbon and water saturation, which suggests that it could be important to consider these parameters in assessing these intervals or close analogs. Full article

The expansion of Internet of Things (IoT) networks has enabled real-time data collection and automation across smart cities, healthcare, and agriculture, delivering greater convenience and efficiency; however, exposure to diverse threats has also increased. Machine learning-based Intrusion Detection Systems (IDSs) provide an effective means of defense, yet they require large volumes of data, and the use of raw IoT network data containing sensitive information introduces new privacy risks. This study proposes a novel privacy-preserving synthetic data generation model based on a tabular diffusion framework that incorporates Differential Privacy (DP). Among the three diffusion models (TabDDPM, TabSyn, and TabDiff), TabDiff with Utility-Preserving DP (UP-DP) achieved the best Synthetic Data Vault (SDV) Fidelity (0.98) and higher values on multiple statistical metrics, indicating improved utility. Furthermore, by employing the DisclosureProtection and attribute inference to infer and compare sensitive attributes on both real and synthetic datasets, we show that the proposed approach reduces privacy risk of the synthetic data. Additionally, a Membership Inference Attack (MIA) was also used for demonstration on models trained with both real and synthetic data. This approach decreases the risk of leaking patterns related to sensitive information, thereby enabling secure dataset sharing and analysis. Full article

Lipopolysaccharides (LPS) from Gram-negative bacteria represent a significant challenge across various industries due to their prevalence and pathogenicity and the limitations of existing detection methods. Traditional approaches, such as the rabbit pyrogen test (RPT) and the Limulus Amebocyte Lysate (LAL) assay, have served as gold standards for endotoxin detection. However, these methods are constrained by high costs, lengthy processing times, environmental concerns, and the need for significant reagent volumes, which limit their scalability and application in resource-limited settings. In this study, we introduce an innovative microfluidic platform that integrates the LAL assay within microdroplets, addressing the critical limitations of traditional techniques. By leveraging the precise fluid control and reaction isolation offered by microdroplet technology, the system reduces reagent consumption, enhances sensitivity, and enables high-throughput analysis. Calibration tests were performed to validate the platform’s ability to detect LPS, using colorimetric measurements. Results demonstrated comparable or improved performance relative to traditional systems, achieving lower detection limits and greater accuracy. This work demonstrates a proof-of-concept miniaturisation of the pharmacopoeial LAL assay. The method yielded low intra-assay variability (σ ≈ 0.002 OD; CV ≈ 0.9% over n = 50 droplets per point) and a LOD estimated from calibration statistics after path-length normalisation. Broader adoption will require additional comparative validation and standardisation. This scalable, cost-effective, and environmentally sustainable approach offers a practical solution for endotoxin detection in clinical diagnostics, biopharmaceutical production, and environmental monitoring. The proposed technology paves the way for advanced LPS detection methods that meet stringent safety standards while improving efficiency, affordability, and adaptability for diverse applications. Full article

The wide use of antibiotics in multiple fields leads to their entry into the environment, challenging agriculture and ecology and potentially affecting maize seedling growth. In this study, maize variety Longken 10 was chosen as the experimental material. Subsequently, two antibiotics commonly utilized in production, namely oxytetracycline (OTC) belonging to the tetracycline class and ciprofloxacin (CIP) from the quinolone class, were selected. To comprehensively examine the impacts of these antibiotics on the phenotype, photosynthetic enzymes, nitrogen metabolism, and endogenous hormone contents of maize seedlings, a series of different concentration gradients (0, 3, 5, 30, 60, and 120 mg·L⁻¹) were established, and the nutrient solution hydroponic method was employed. The results showed that, compared with the control group (CK), the activities of all indicators of maize seedlings were the strongest and the seedling growth was the most vigorous when the concentration of CIP was 5 mg·L⁻¹ and that of OTC was 3 mg·L⁻¹. The inhibitory effect of OTC on various indicators of maize seedlings was stronger than that of CIP. The underground parts of maize seedlings were more sensitive to OTC and CIP than the aboveground parts. Overall, maize seedlings exhibited a trend where high concentrations (30–120 mg·L⁻¹) of antibiotics inhibited growth, while low concentrations (3–5 mg·L⁻¹) promoted growth. The treatment groups with 3–5 mg·L⁻¹ of OTC and CIP increased maize seedling growth phenotypes, the robust growth of seedlings with enhanced vitality, and the relative water content of maize leaves; decreased the relative electrical conductivity of maize leaves, indicating reduced cell permeability; increased the activities of leaf photosynthetic enzymes (PEPCase, RUBPCase, PPDK, NADP-ME, and NADP-MDH); increased the levels of hormones (IAA, GA, and ZR) in maize leaves and roots; decreased the levels of ABA and MeJA; increased the levels of nitrogen metabolism-related enzymes (GS, GOGAT, and GAD) in roots and leaves; decreased the GDH level; enhanced root activity and increased various root parameters (including average diameter, number of root tips, total volume, total root length, and root surface area), indicating vigorous root growth. Compared with CK, the treatment groups with 30–120 mg·L⁻¹ of OTC and CIP reduced the phenotypes of maize seedlings, decreased the relative water content of maize leaves and increased the relative electrical conductivity of maize leaves, indicating enhanced cell permeability; reduced the activity of leaf photosynthetic enzymes, leading to weakened photosynthesis and decreased photosynthetic productivity; lowered the levels of IAA, GA, and ZR in leaves and roots of maize seedlings, and increased the levels of ABA and MeJA; decreased the levels of GS, GOGAT, and GAD in leaves and roots of maize seedlings, and increased the GDH level; reduced root activity, with the corresponding decrease in various root parameters. Full article

In some projects, a lack of data causes problems for presenting an accurate prediction model for surface settlement caused by shield tunneling. Existing models often rely on large volumes of data and struggle to maintain accuracy and reliability in shield tunneling. In particular, the spatial dependency between adjacent rings is overlooked. To address these limitations, this study presents a small-sample prediction framework for settlement induced by shield tunneling, using an adjacent-ring graph convolutional network (GCN-SSPM). Gaussian smoothing, empirical mode decomposition (EMD), and principal component analysis (PCA) are integrated into the model, which incorporates spatial topological priors by constructing a ring-based adjacency graph to extract essential features. A dynamic ensemble strategy is further employed to enhance robustness across layered geological conditions. Monitoring data from the Wuhan Metro project is used to demonstrate that GCN-SSPM yields accurate and stable predictions, particularly in zones facing abrupt settlement shifts. Compared to LSTM+GRU+Attention and XGBoost, the proposed model reduces RMSE by over 90% (LSTM) and 75% (XGBoost), respectively, while achieving an R² of about 0.71. Notably, the ensemble assigns over 70% of predictive weight to GCN-SSPM in disturbance-sensitive zones, emphasizing its effectiveness in capturing spatially coupled and nonlinear settlement behavior. The prediction error remains within ±1.2 mm, indicating strong potential for practical applications in intelligent construction and early risk mitigation in complex geological conditions. Full article

Personalized, point-of-care testing of human tears is essential for ocular disease diagnosis, yet it is hampered by picomolar biomarker levels and microliter sample volumes. In this work, we developed an integrated, portable electrochemiluminescence (ECL)-based device for rapid and quantitative detection of tumor necrosis factor alpha (TNF-α), a pivotal inflammatory marker in ocular surface disease, with particular relevance to dry eye syndrome (DES). The device integrates a miniaturized electrochemical cell for ECL reactions and a compact silica photomultiplier for signal measurement. A vertical silica mesochannel (VSM)-coated ITO electrode is also integrated and further functionalized with TNF-α-specific aptamers. The VSM enables the enrichment of ECL luminophores, thus enabling further amplification of ECL signals and enhancing sensitivity. A wide linear range from 0.1 to 200 pg/mL was achieved using 10-fold dilution of 3 μL tear samples. Overall, this study provides a portable, highly sensitive platform for personalized analysis of TNF-α in tear fluid, enabling rapid point-of-care assessment of DES. Full article

Sales competition and recycling rivalry are critical factors affecting the operation of closed-loop supply (CLSC). The existing research on competitive CLSCs primarily analyzes the impact of competition between two sales entities and/or two recycling entities on management decisions. To make the study more realistic, this study constructs a Stackelberg game model with the manufacturer as a leader, and analyzes the impacts of competition among $n$ retailers ($\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} n\ge 2$) and rivalry among $m$ third-party recyclers $(\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} m\ge 2)$ on the decision-making and profits of both node enterprises and the supply chain system, and proposes a linear transfer-payment contract to coordinate the CLSC from an economic perspective. Numerical analyses are conducted to visualize the effects of competition on the decisions and profits. The key findings are as follows: (1) In the centralized system, inter-retailer competition reduces optimal order quantities but does not affect optimal retail prices. In the decentralized system, however, it decreases both optimal order quantities and retail prices. (2) Rivalry among recyclers reduces their optimal recycling volumes but does not affect their optimal recycling prices in the centralized system. In the decentralized system, however, such rivalry not only decreases recycling volumes but also increases optimal recycling prices. (3) The manufacturer’s product wholesale price and used product recycling price remain independent of competitive interactions among retailers and recyclers in the decentralized system. (4) Competition among retailers and recyclers positively affects the profits of the CLSC and the manufacturer, but negatively impacts those of retailers and recyclers. (5) When the reward–penalty factors for product order and used product recycling fall within a specific range, the linear transfer-payment contract can coordinate the CLSC in the presence of competition in both retail and recycling. (6) All enterprises’ profits are sensitive to the penalty–reward factor, but this sensitivities also gradually decrease as the number of retailers and (or) recyclers increases. Full article

Although significant progress has been made in the global medical level, cardiovascular diseases still pose a serious threat to human life and health. Among many cardiovascular diseases, acute myocardial infarction (AMI) is particularly severe. If not treated in a timely manner, it may lead to serious consequences such as cardiac arrest and sudden death. Early diagnosis of myocardial infarction (MI) is an important means of preventing and controlling the mortality rate of AMI. Creatine kinase isoenzyme (CK-MB) is a key biomarker of MI. It rises rapidly within 2 h after myocardial injury, reaches its peak at 24 h, and returns to normal at 72 h. Furthermore, CK-MB has a high specificity in monitoring secondary MI. Therefore, the early, real-time, and accurate detection of CK-MB is of great significance for the prevention, diagnosis, and prognosis of AMI. Conventional CK-MB detection methods have problems such as false positive elevation, large blood sampling volume, long time consumption, and complex operation, making it difficult to meet the needs of point-of-care testing (POCT). Biosensor technology, with its low cost, high sensitivity, and portability, offers a promising solution for point-of-care CK-MB testing, thereby greatly aiding AMI diagnosis. Full article

In this study, a numerical model consisting of high-resolution hydrodynamic and Lagrangian particle tracking modules based on the Finite-Volume Coastal Ocean Model framework was established to simulate the hydrodynamic conditions and characteristics of the sedimentation of aquaculture-derived organic matter (AOM) from cage aquaculture in Sansha Bay. The results showed that Sansha Bay was characterized by regular semidiurnal tides and large tidal ranges. Reciprocating currents with main currents directed northward and southward during the rising and falling tides, respectively, predominated the main channels of the bay. Residual feed had larger settling velocities than feces. The maximal dispersion distances of residual feed and feces during the spring tide were 217.1 and 1805.7 m, respectively, three times those during the neap tide (74.2 and 675.6 m, respectively). During the spring tide, the largest dispersion distance of AOM occurred at the rush moment. The AOM movement trajectories were mainly controlled by the main currents. Both the tidal structure and current characteristics affected the AOM sedimentation in Sansha Bay. The sedimentation characteristics of AOM were unrelated to feeding intensity. The results of simulations agreed with the field observations in this study, suggesting that the estimated model had a good accuracy and sensitivity. Full article

Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has investigated the use of supervised learning with large volumes of labeled data to improve segmentation across medical imaging modalities and unsupervised learning with unlabeled data to segment without detailed annotations. However, a significant hurdle remains in constructing a model that can segment diverse medical images in a zero-shot manner without any annotations. In this work, we introduce the attention diffusion zero-shot unsupervised system (ADZUS), a new method that uses self-attention diffusion models to segment biomedical images without needing any prior labels. This method combines self-attention mechanisms to enable context-aware and detail-sensitive segmentations, with the strengths of the pre-trained diffusion model. The experimental results show that ADZUS outperformed state-of-the-art models on various medical imaging datasets, such as skin lesions, chest X-ray infections, and white blood cell segmentations. The model demonstrated significant improvements by achieving Dice scores ranging from 88.7% to 92.9% and IoU scores from 66.3% to 93.3%. The success of the ADZUS model in zero-shot settings could lower the costs of labeling data and help it adapt to new medical imaging tasks, improving the diagnostic capabilities of AI-based medical imaging technologies. Full article

Following World War II, the rapid expansion of construction led to intensive use of natural resources, leading to resource depletion and accelerating climate change. Prioritising sustainability in structural design has therefore become essential. This study investigates three reinforced concrete (RC) slab systems typical of office buildings: flat slab, beam and slab, and two-way joist slab, using Eurocode 2 design principles. A 3 × 3 bay model with spans from 4 m to 14 m and three concrete grades (C25/30, C32/40, C40/50) was analysed through nonlinear finite element modelling. The methodology uniquely combines structural optimisation with embodied carbon and cost assessments across multiple slab typologies and span configurations, an approach rarely addressed in prior research. Results show that two-way joist slabs achieve the most favourable balance, reducing embodied carbon by 25–35% and construction cost by up to 15% compared to flat and beam and slab systems. This advantage is particularly evident at spans of 10 m or more, where the ribbed geometry significantly reduces concrete volume. Flat slabs are cost-efficient for short spans of up to 8 m but incur up to 40% higher carbon at longer spans due to increased thickness and punching shear reinforcement requirements. Beam and slab systems consistently recorded the highest cost and carbon values, offering limited environmental benefits despite their structural stiffness. The findings provide practical guidance for span-sensitive slab selection in early design, enabling the delivery of reinforced concrete buildings that are both cost-effective and environmentally responsible. Full article

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as biogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, and control of critical factors for biogas production, as well as the biodigester’s capacity and other relevant elements. In practice, the dosing of waste is conducted empirically, leading to inefficiencies that limit the potential for biogas production in real scenarios. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, considering fundamental parameters such as the moisture percentage, pH, carbon/nitrogen ratio (C/N), substrate volume, organic matter, volatile solids (VS), and biogas production potential from different wastes. The model estimates the optimal waste composition based on the biodigester capacity to ensure balanced substrates. The results for the proposed scenarios demonstrate its effectiveness: Scenario 1 achieved 3.42 m³ (3418.67 L) of biogas, while Scenario 2, with a greater diversity of waste, reached 8.06 m³ (8061.43 L). The model maintained pH (6.49–6.50), C/N ratio (20.00), and moisture (60.00%) within optimal ranges. Additionally, a Monte Carlo sensitivity analysis (1000 simulations) validated its robustness with a 95% confidence level. This model provides an efficient tool for optimizing biogas production and waste dosing in rural contexts, promoting clean and sustainable technologies for renewable energy generation. Full article

In neonatal care, donor human milk (DHM) is used when maternal milk is unavailable or insufficient. In several countries, including Germany, raw (i.e., unpasteurised) DHM is occasionally administered under specific clinical conditions. However, the lack of standardised, evidence-based microbiological testing protocols raises concerns about the reliability of safety assessments for this high-risk patient group. The objective of this study was to assess the performance of four culture-based microbiological methods for detecting Enterobacterales in donor human milk, using both spiked samples and raw milk. We compared the detection limits of four culture-based microbiological methods, with and without enrichment, using spiked DHM samples and 93 raw DHM samples from a single donor (limited generalisation). Artificially inoculated samples contained defined concentrations of E. coli, K. pneumoniae, and S. ureilytica. Detection limits varied by several orders of magnitude (2.86 × 10² CFU/mL to 4.90 × 10⁰ CFU/mL). In real samples, enrichment-based methods detected Gram-negative pathogens in four out of ninety-three samples (three S. ureilytica, one P. juntendi); direct plating detected none. Increasing the sample volume and applying enrichment improved detection sensitivity. Whole-genome sequencing confirmed species identity and showed that the S. ureilytica isolates from a single donor were clonally related, indicating a recurring detection pattern and underscoring the need for longitudinal microbiological monitoring. In view of the new EU SoHO Regulation classifying DHM as a Substance of Human Origin, these findings highlight the urgent need for standardised, sensitive protocols to ensure neonatal safety. Full article

This pilot study evaluated the effects of cannabidiol (CBD) oil supplementation on growth performance, stress biomarkers, and haematological profiles in dairy calves undergoing the weaning transition. Nineteen Holstein calves were divided into two paternal-sibling groups: a CBD-supplemented experimental group (n = 10) and a CON-control group (n = 9). The CBD group received 5 mL/head/day of CBD oil for the first two days (pre-weaning), followed by 10 mL/head/day for three consecutive days post-weaning. Body weight increased significantly over time in both groups (p = 0.000); nevertheless, no significant differences were observed between groups (p = 0.173) or for the group × time interaction (p = 0.929), indicating that CBD did not affect overall growth trajectory. However, a significant group × time interaction (p = 0.006) for average daily gains in the CBD group was observed. Serum cortisol concentrations were significantly lower in CBD-supplemented calves at Day 0 and +2 days, compared to the CON group, indicating a transient anti-stress effect (p = 0.043 for group effect). At +5 days, cortisol levels in the CBD group increased, surpassing control values, though this difference was not significant. A trend-level group × time interaction (p = 0.067) suggested a distinct temporal cortisol response in CBD-treated calves. Immune cell counts (LYM, MON, NEU) showed no significant differences, though monocyte levels trended lower in CBD calves at early time points. Platelet indices revealed a significant reduction in mean platelet volume (p = 0.047) and stable PDWc and plateletcrit values in the CBD group, suggesting modulation of inflammatory status. Alanine aminotransferase levels increased over time with a significant group effect (p = 0.014), indicating a mild hepatic response, while glucose and alkaline phosphatase remained within physiological ranges. These findings suggest that short-term CBD supplementation may transiently modulate stress and inflammatory responses during weaning, with potential benefits for physiological resilience. However, rebound endocrine effects and hepatic sensitivity highlight the need for further research to refine dosing strategies and assess long-term safety in dairy production systems. Full article