Search Results (4,347)

Additive manufacturing is driving a significant change in industry, extending beyond prototyping to the inclusion of printed parts in final designs. Stereolithography (SLA) is a polymerization technique valued for producing highly detailed parts with smooth surface finishes. This study presents a hybrid intelligent framework for modeling and optimizing the SLA 3D printer process’s parameters for Acrylonitrile Butadiene Styrene (ABS) photopolymer parts. The nonlinear relationships between the process’s parameters (Orientation, Lifting Speed, Lifting Distance, Exposure Time) and multiple performance characteristics (ultimate tensile strength, yield strength, modulus of elasticity, Shore D hardness, and surface roughness), which represent complex relationships, were investigated. A Taguchi design of the experiment with an L18 orthogonal array was employed as an efficient experimental design. A novel hybrid fuzzy logic–Particle Swarm Optimization (PSO) algorithm, ARGOS (Adaptive Rule Generation with Optimized Structure), was developed to automatically generate high-accuracy Mamdani-type fuzzy inference systems (FISs) from experimental data. The algorithm starts by customizing Modified Learn From Example (MLFE) to create an initial FIS. Subsequently, the generated FIS is tuned using PSO to develop and enhance predictive accuracy. The ARGOS models provided excellent performances, achieving correlation coefficients (R²) exceeding 0.9999 for all five output responses. Once the FISs were tuned, a multi-objective optimization was carried out based on the weighted sum method. This step helped to identify a well-balanced set of parameters that optimizes the key qualities of the printed parts, ensuring that the results are not just mathematically ideal, but also genuinely helpful for real-world manufacturing. The results showed that the proposed hybrid approach is a robust and highly accurate method for the modeling and multi-objective optimization of the SLA 3D process. Full article

Background/Objectives: Prediabetes markedly increases the risk of progression to type 2 diabetes. While exercise and dietary polyphenols independently enhance metabolic health, their combined and synergistic effects remain unclear. This randomized, single-blind, placebo-controlled trial investigated the synergistic effects of concurrent training and a microencapsulated persimmon–karonda polyphenol formulation on glycemic control and inflammatory outcomes in adults with prediabetes and who are overweight/obese. Methods: Forty-three participants completed the intervention and were assigned to placebo, concurrent training (CBT), supplementation (EATME), or the combined intervention (CBT + EATME) for 8 weeks. Primary outcomes included fasting blood glucose (FBG), glycated hemoglobin (HbA1c), homeostatic model assessment for insulin resistance (HOMA-IR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), adiponectin, physical fitness, and quality of life (QoL). Results: All intervention groups (CBT, EATME, and CBT + EATME) showed improvements in glycemic indices, with the greatest reductions in FBG (p < 0.01), HbA1c (p < 0.05), and HOMA-IR (p < 0.01) observed in the CBT + EATME group compared with placebo. All interventions significantly reduced hs-CRP (p < 0.01) and IL-6 (p < 0.01), accompanied by marked increases in adiponectin (p < 0.01), compared with placebo. In the CBT + EATME group, reductions in hs-CRP were positively correlated with improvements in HOMA-IR (r = 0.627, p < 0.05). Both CBT and CBT + EATME improved muscular strength and maximal oxygen consumption (V̇O₂max), with the combined intervention producing greater gains in upper- and lower-body strength (p < 0.05), V̇O₂max (p < 0.05), and the psychological well-being domain of QoL (p < 0.05) compared with placebo. Conclusions: These findings highlight that combining concurrent training with microencapsulated polyphenol supplementation produced the most consistent improvements across metabolic, inflammatory, and fitness outcomes, supporting this combined approach as an integrated and synergistic strategy to reduce diabetes risk and promote overall health in at-risk adults. The trial was registered at the Thai Clinical Trials Registry (TCTR20250512003). Full article

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality. Full article

In the context of carbon neutrality, ammonia-coal co-firing is considered an effective way to reduce emissions from coal-fired units. This paper takes a 125 MW tangential combustion boiler as the research object and combines CFD and CHEMKIN models to study the effects of ammonia injection position (L1–L3) and blending ratio (0–30%) on combustion characteristics and NO generation. The results indicate that L1 (same-layer premixed injection) can form a continuous and stable flame structure and maintain low NO emissions. L2 (fuel-staged configuration) shows the highest burnout rate and strong denitration potential under high mixing conditions, while L3 has an unstable flow field and the worst combustion structure. NO emissions show a typical “first rise and then fall” trend with the blending ratio. L1 performs optimally in the range of 15–20%, and L2 peaks at 20%. Mechanism analysis indicates that R430 is the main NO generation reaction, while R15 and R427 dominate the NO reduction process. The synergistic reaction between NH_x free radicals and coke can effectively inhibit the formation of NO and improve combustion efficiency. Full article

Traditional network threat detection based on signatures is becoming increasingly inadequate as network threats and attacks continue to grow in their novelty and sophistication. Such advanced network threats are better handled by anomaly detection based on Machine Learning (ML) models. However, conventional anomaly-based network threat detection with traditional ML and Deep Learning (DL) faces fundamental limitations. Graph Neural Networks (GNNs) and Transformers are recent deep learning models with innovative architectures, capable of addressing these challenges. Reinforcement learning (RL) can facilitate adaptive learning strategies for GNN- and Transformer-based Intrusion Detection Systems (IDS). However, no systematic literature review (SLR) has jointly analyzed and synthesized these three powerful modeling algorithms in network threat detection. To address this gap, this SLR analyzed 36 peer-reviewed studies published between 2017 and 2025, collectively identifying 56 distinct network threats via the proposed threat classification framework by systematically mapping them to Enterprise MITRE ATT&CK tactics and their corresponding Cyber Kill Chain stages. The reviewed literature consists of 23 GNN-based studies implementing 19 GNN model types, 9 Transformer-based studies implementing 13 Transformer architectures, and 4 RL-based studies with 5 different RL algorithms, evaluated across 50 distinct datasets, demonstrating their overall effectiveness in network threat detection. Full article

This study examines the potential use of rejected and discarded grains from the common bean industry as a starch source for producing plasticized films with glycerol. The observed morphological characteristics of starch granules from discarded grains were diverse, with round, oval, and kidney-like shapes and sizes ranging from 7 to 34 µm. We determined the pasting profile: the pasting temperature (GT) fell between 72 °C and 74 °C, while the peak viscosity (P_v) demonstrated a significant rise at 10% and 15% starch concentrations. To better understand pasting behavior, mathematical modeling was employed to predict P_v behavior, with an R² value of 0.98. All film formulations were successful, yielding transparent, homogeneous, odorless, flexible films with smooth surfaces. Scanning electron microscopy analysis of the films revealed a flawless surface devoid of fissures, cracks, and pores, displaying a rough texture with a consistent structure and some starch granules resembling empty sacks due to amylose and amylopectin leaching. The highest tensile strength was observed with 6% starch and 1.5 mL of glycerol and the lowest with 4.5% starch and 3.9 mL of glycerol. The findings suggest that starch derived from discarded grains from the bean industry has unique characteristics and properties, making it a promising alternative source for intelligent packaging development. Full article

Background and Objectives: We examined how health-related quality of life (HRQoL) in children with congenital heart disease (CHD) and caregiver stress/burnout relate in terms of lesion severity. Methods: We enrolled 72 child–caregiver dyads at a tertiary centre (May 2023–April 2025). Children completed PedsQL and CDI (anxiety assessment via SCARED-C was descriptive and not modelled in APIM); caregivers completed SF-36, PSS-10, and the Parental Burnout Assessment (PBA). Lesion severity (mild n = 22, moderate n = 34, severe n = 16) and LVEF were abstracted. Pearson correlations and actor–partner interdependence models (APIM) estimated within- and cross-partner effects. Results: Child PedsQL decreased with severity (mild 81.2 ± 7.4; moderate 70.9 ± 8.1; severe 63.3 ± 5.1; p < 0.001); caregiver SF-36 Mental Component Summary (MCS) showed a parallel gradient (66.8 ± 9.2; 59.7 ± 8.5; 54.1 ± 7.9; p < 0.001). Child HRQoL correlated with caregiver MCS (r = 0.46) and inversely with caregiver stress (PSS r = −0.42) and burnout (PBA r = −0.39). In APIM, caregiver stress predicted a caregiver’s own MCS (actor β = −0.38, p < 0.001) and the child’s PedsQL (partner β = −0.26, p = 0.002); higher child depressive symptoms predicted lower caregiver MCS (partner β = −0.22, p = 0.006). Each step up in lesion severity independently reduced child PedsQL by 7.9 points and caregiver MCS by 5.3 points (both p < 0.001). Dyads with unscheduled hospitalizations (n = 43) had poorer scores in both members. Conclusions: Psychological wellbeing in CHD dyads is strongly interdependent; caregiver stress relates to lower child HRQoL and child mood to caregiver mental health. Brief dyadic screening (PedsQL/SF-36 with PSS/CDI) and integrated, family-focused interventions may better target high-risk families, particularly with severe lesions or recent hospitalizations. Full article

Benefit/risk management of skin exposure to sunlight, especially ultraviolet (UV) rays, is mainly driven by photoaging, cancer incidence, and the requirement for vitamin D3 synthesis. Antioxidant phytocompounds are considered to be a valuable source of molecules to protect skin from UV-induced damage, but their impact on other UV-related metabolic pathways is rarely described. In this study, an indigoid-rich Persicaria tinctoria extract (PTE) was evaluated on three consequences of UV exposure: DNA damage and inflammation, vitamin D3 content, and melanogenesis. A moderate UV exposure was applied on skin models, corresponding to approximately 1 h exposure in the spring in western Europe. UV-induced DNA damage and inflammation were measured through the quantification of cyclobutane pyrimidine dimers (CPDs) and cytokines. Response to heat stress was quantified through the release of prostaglandin. Then, the impact of PTE on vitamin D3 and melanin synthesis was observed. PTE decreased by −56% in the number of cells presenting CPDs. PTE decreased the production of pro-inflammatory cytokine IL-6 (−59%) and stimulated the release of the protective cytokine IL-1Ra (+49%). It decreased PGE₂ release by −27%. In skin explants, PTE boosted the vitamin D3 concentration (+345%). Several genes involved in melanogenesis were up-regulated by PTE (MC1R × 2.46, MITF × 1.69, TYR × 2.06, MLPH × 1.53). It promoted melanin content by +126% and by +86% when associated with SPF 30. The extract decreased the amount of protective eumelanin, leading to visible skin tanning of reconstructed human epidermis (L*-15%, ITA −125%). As a new finding, PTE minimized DNA damage and inflammation caused by a daily dose of UV, and surprisingly, promoted vitamin D3 and eumelanin synthesis, suggesting that it represents an opportunity to reconcile skin protection and the physiological need for sunlight. Full article

Background: Strobilurin fungicides (SFs) are widely detected in the environment, but data on their occurrence in humans and potential health effects are scarce. Objective: This study aimed to characterize the exposure to SFs in a human population from South China and to investigate their potential association with biomarkers of oxidative stress and liver damage. Methods: In a cross-sectional study, we analyzed serum samples from healthy participants and secondary nonalcoholic fatty liver disease (S-NAFLD) patients. Concentrations of SFs and oxidative stress biomarkers including 8-iso-prostaglandin-F2α (8-PGF_2α), 11β-prostaglandin F2α (11-PGF_2α), 15(R)-prostaglandin F2α (15-PGF_2α), and 8-oxo-7,8-dihydro-20-deoxyguanosine (8-OHdG) were measured. Associations between SF exposure, liver function biomarkers, and S-NAFLD prevalence were assessed using multivariate regression models. A mediation analysis was conducted to explore the role of oxidative stress. Results: Azoxystrobin (AZ), fluoxastrobin (FLUO), and fenamidone (FE) were the predominant compounds, with median concentrations ranging from 0.016 to 0.042 ng/mL. Significant positive correlations were observed between all frequently detected SFs and oxidative stress biomarkers (p < 0.05). FE was associated with a modest, albeit statistically significant, prevalence of S-NAFLD. AZ and FE were also found to be statistically significantly associated with altered levels of direct bilirubin (DBIL, FDR-q < 0.05). The exploratory mediation analysis indicated a statistically significant indirect effect (17.1% to 31.2%), suggesting that lipid peroxidation biomarkers could serve as potential mediators between AZ exposure and DBIL levels. Conclusions: This study provides the first evidence of widespread SF exposure in a South Chinese population and reveals significant associations with oxidative stress and AZ exposure with liver function biomarkers (i.e., DBIL), with exploratory analyses suggesting a potential mediating role of oxidative stress in this relationship. However, the cross-sectional design precludes causal inference, and the modest effect sizes warrant cautious interpretation. These findings highlight the need for further longitudinal research to confirm the hepatotoxicity of SFs in humans. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

We estimated, for the first time, the age of Diaphus watasei (Jordan & Starks, 1904) in the South China Sea (SCS) based on otolith microstructure. According to one-way ANOVA, differences were not observed between the sexes with regard to standard length, body mass, or age. Based on 137 specimens, the sex ratio and relationship between standard length and body mass was 1.32:1 (male/female) and W = 0.0000433L^2.78 (r² = 0.923), respectively. The von Bertalanffy model was fitted as L_t = 171.38 [1 − exp(−0.00206(t − 3.82))], r² = 0.645 (n = 92), which indicated a maximum growth rate of 0.356 mm/day. The speculated birth date of the 92 specimens of D. watasei occurred across almost all months of the year. Full article

Thise article examines the reliable online detection and IDS/IPS intrusion prevention in dynamic corporate networks problems, where traditional signature-based methods fail to keep pace with new and evolving attacks, and streaming data is susceptible to drift and targeted “poisoning” of the training dataset. As a solution, we propose a hybrid neural network system with adaptive online training, a formal minimax false-positive control framework, and a robustness mechanism set (a Huber model, pruned learning rate, DRO, a gradient-norm regularizer, and a prioritized replay). In practice, the system combines modal encoders for traffic, logs, and metrics; a temporal GNN for entity correlation; a variational module for uncertainty assessment; a differentiable symbolic unit for logical rules; an RL agent for incident prioritization; and an NLG module for explanations and the preparation of forensically relevant artifacts. In this case, the applied components are connected via a cognitive layer (cross-modal fusion memory), a Bayesian-neural network fuser, and a single multi-task loss function. The practical implementation includes the pipeline “novelty detection → active labelling → incremental supervised update” and chain-of-custody mechanisms for evidential fitness. A significant improvement in quality has been experimentally demonstrated, since the developed system achieves an ROC AUC of 0.96, an F1-score of 0.95, and a significantly lower FPR compared to basic architectures (MLP, CNN, and LSTM). In applied validation tasks, detection rates of ≈92–94% and resistance to distribution drift are noted. Full article

Reinforcement learning (RL) agents face significant challenges in sparse-reward environments, as insufficient exploration of the state space can result in inefficient training or incomplete policy learning. To address this challenge, this work proposes a teacher–student framework for RL that leverages the inherent knowledge of large language models (LLMs) to decompose complex tasks into manageable subgoals. The capabilities of LLMs to comprehend problem structure and objectives, based on textual descriptions, can be harnessed to generate subgoals, similar to the guidance a human supervisor would provide. For this purpose, we introduce the following three subgoal types: positional, representation-based, and language-based. Moreover, we propose an LLM surrogate model to reduce computational overhead and demonstrate that the supervisor can be decoupled once the policy has been learned, further lowering computational costs. Under this framework, we evaluate the performance of three open-source LLMs (namely, Llama, DeepSeek, and Qwen). Furthermore, we assess our teacher–student framework on the MiniGrid benchmark—a collection of procedurally generated environments that demand generalization to previously unseen tasks. Experimental results indicate that our teacher–student framework facilitates more efficient learning and encourages enhanced exploration in complex tasks, resulting in faster training convergence and outperforming recent teacher–student methods designed for sparse-reward environments. Full article

A deflection prediction approach based on an adaptive MEC–RBF neural network was developed in this study. By dynamically optimizing the centres, widths, and weights of the RBF network, the proposed method substantially increases the prediction accuracy, and it achieves an R² of 0.9789 and an RMSE of 1.4978 on the training dataset. It effectively resolves the stability challenges that are associated with nonlinear construction conditions in traditional models. An orthogonal experimental design analysis revealed that the girder block length and the cantilever-to-span length ratio (d/L) were the most influential factors that affected deflection, whereas the effects of uniformly distributed loads and temperature were negligible, thereby providing a sound basis for parameter simplification. The application of the model to the Hannan Yangtze River Bridge yielded a maximum discrepancy of only 5.56 mm (17.7% error rate) between the predicted and measured values, thus demonstrating its practical engineering reliability. By innovatively integrating intelligent optimization techniques with neural networks, this approach overcomes the limitations in terms of real-time responsiveness and long-term stability of conventional methods and offers an efficient and reliable technical tool for the control of large-scale bridge construction. Full article

A large body of evidence suggests that flaxseed oil (FSO), one of the richest sources of essential omega-3 fatty acids, has neuroprotective properties. Purinergic signaling plays a crucial role in pathophysiological processes in the nervous system. There is a lack of evidence regarding the effects of FSO on the purinergic system under both physiological and neurotoxic conditions. Here we report the effects of dietary FSO consumption in a rat model of trimethyltin (TMT) intoxication. Exposure to TMT selectively induces hippocampal neuronal damage and glial reactivation associated with oxidative stress and neuroinflammation, causing severe behavioral impairments. When administered orally (1 mL/kg) before and during TMT intoxication (single dose 8 mg/kg, i.p.) to female Wistar rats, FSO effectively prevented the behavioral disturbances induced by TMT. FSO selectively increased CAT-mRNA level in both healthy and TMT-intoxicated animals, while preventing TMT-induced upregulation of Nrf2, NF-κB, and GPx1 without affecting SOD2 or Gsr-mRNA levels. FSO prevented microgliosis, microglial NTPDase1-eN upregulation, and the increase in purinergic receptors involved in microglial reactivity. Pretreatment with FSO in TMT-intoxicated rats maintained the activity and expression of NTPDase1 at control level, while the activity and expression of eN and ADA were increased. FSO upregulated eN, A₁R, A_2BR, A₃R, ADA, and NGF, while downregulating NTPDase1, A_2AR, and ENT1 in TMT-intoxicated rats. This suggests complex modulation of purinergic signaling, particularly the adenosine system. These findings may contribute to a better understanding of the effects of FSO, highlighting the impact of the dietary intake of this oil on the brain. Full article