Search Results (34,208)

In engineering structures, damping materials are an effective way to improve vibration characteristics, but they can significantly increase the weight and cost of the structure. In this study, based on the variable density topology optimization algorithm, combined with finite element simulation and experimental validation, the vibration damping performance of a composite structure with steel plate and damping material is optimized. With the objective of minimizing the resonance response and the constraint of damping material volume, the material distribution of the damping layer is optimized, and the amount of damping material used is successfully reduced by 31.2%. By building a test rig and comparing the vibration responses under the three working conditions of no damping, full damping coverage, and optimized damping, the effectiveness of the optimization strategy is verified, and a significant reduction in vibration response is achieved. This study provides an innovative solution for lightweight design and cost control in engineering. Full article

This study investigated how propagation systems, growth regulators, and hormone formulations interactively affect the rooting and subsequent growth of rosemary (Salvia rosmarinus Spenn) cuttings. A three factorial (3 × 2 × 7) experiment was conducted under a fully controlled greenhouse environment, incorporating three soilless propagation systems (mist, float, aeroponics), two rooting hormone formulations (powder and gel-based IBA), and two growth regulators (paclobutrazol and daminozide) at three concentrations each. Significant differences (p < 0.001) were found in shoot height, root length, and number of lateral roots. The float system combined with powder hormone and no retardants achieved the highest shoot height (mean = 16.7 cm), while aeroponics with powder hormone and daminozide 1000 ppm promoted the greatest root branching (mean = 12.2 lateral roots per cutting). Root length was maximized (mean = 15.9 cm) under float systems with daminozide 1000 ppm. High doses of both growth regulators negatively affected all parameters across systems. Post-transplantation monitoring confirmed that cuttings from float and mist systems treated with powder hormone and low or no growth retardants exhibited superior establishment and net growth over 60 days. These findings demonstrate the critical importance of pairing hormone type, regulator concentration, and propagation system, providing actionable protocols for nursery managers aiming to enhance Salvia rosmarinus propagation in commercial practice. Full article

The decoding of motor imagery (MI) electroencephalogram (EEG) signals is crucial for motor control and rehabilitation. However, as feature extraction is the core component of the decoding process, traditional methods, often limited to single-feature domains or shallow time-frequency fusion, struggle to comprehensively capture the spatio-temporal-frequency characteristics of the signals, thereby limiting decoding accuracy. To address these limitations, this paper proposes a dual-branch neural network architecture with multi-domain feature fusion, the dual-branch spatio-temporal-frequency fusion convolutional network with Transformer (DB-STFFCNet). The DB-STFFCNet model consists of three modules: the spatiotemporal feature extraction module (STFE), the frequency feature extraction module (FFE), and the feature fusion and classification module. The STFE module employs a lightweight multi-dimensional attention network combined with a temporal Transformer encoder, capable of simultaneously modeling local fine-grained features and global spatiotemporal dependencies, effectively integrating spatiotemporal information and enhancing feature representation. The FFE module constructs a hierarchical feature refinement structure by leveraging the fast Fourier transform (FFT) and multi-scale frequency convolutions, while a frequency-domain Transformer encoder captures the global dependencies among frequency domain features, thus improving the model’s ability to represent key frequency information. Finally, the fusion module effectively consolidates the spatiotemporal and frequency features to achieve accurate classification. To evaluate the feasibility of the proposed method, experiments were conducted on the BCI Competition IV-2a and IV-2b public datasets, achieving accuracies of 83.13% and 89.54%, respectively, outperforming existing methods. This study provides a novel solution for joint time-frequency representation learning in EEG analysis. Full article

Guaranteeing food safety in the European Union (EU) is a continuing issue affected by diverse national traditions, regulatory power, and consumer culture. Despite the presence of a harmonized regulatory context, there continues to be variability in performance among the 27 member states. This study gives an extensive comparative evaluation of EU food safety based on three indicators: Rapid Alert System for Food and Feed (RASFF) alerts, pesticide maximum-residue-limit (MRL) violation, and per capita food loss. Fuzzy TOPSIS, K-means clustering, and scenario-based sensitivity tests are used to give an extensive appraisal of the performance of member states. Alarming differences are quoted as findings of significance. The highest number of RASFF notifications (212) and percentage of pesticide MRL non-compliance (1.5%) were reported in 2022 by Bulgaria, whereas the lowest values were reported by Estonia and Lithuania—15–20 RASFF notifications and less than 0.6% MRL violation rates. A statistically significant correlation (r = 0.72, p < 0.001) between pesticide MRL violation and food safety warnings was confirmed in favor of pesticide regulation as the optimal predictor of food safety warnings. On the other hand, food loss did not significantly affect safety measures but indicated very high variation (from 76 kg/capita per year in Croatia to 142 kg/capita per year in Greece). These findings suggest that while food loss remains an environmental problem, pesticide control is more central to the protection of food safety. Targeted policy is what the research necessitates: intervention and stricter enforcement in low-income countries, and diffusion of best practice from successful states. The composite approach adds to EU food safety policy discourse through the combination of performance indicators and targeted regulatory emphasis. Full article

In recent years, passive acoustic monitoring (PAM) technology has significantly contributed to advancements in aquaculture techniques, system iterations, and increased production yields within intelligent feeding systems for Penaeus vannamei. However, current PAM-based intelligent feeding systems do not incorporate environmental factors into the decision process, limiting the improvement of monitoring accuracy in complex environments such as ponds. To establish a connection between environmental factors and the feeding acoustics of P. vannamei, this study utilized PAM technology combined with video analysis to investigate the effects of three key environmental factors—temperature, ammonia nitrogen, and nitrite nitrogen—on the feeding behavioral characteristics of shrimp, with a specific focus on acoustic signals “clicks”. The results demonstrated a significant correlation between the number of clicks and feed consumption in shrimp across different treatments, establishing this stable relationship as a reliable indicator for assessing shrimp feeding status. When water temperature increased from 20 °C to 32 °C, shrimp feed consumption showed an elevation from 0.46 g to 0.95 g per 30 min, with the average number of clicks increasing from 388 to 2947.58 and sound pressure levels rising accordingly. Conversely, ammonia nitrogen at 12 mg/L reduced feed consumption by 0.15 g and decreased click counts by 911.75 pulses compared to controls, while nitrite nitrogen at 40 mg/L similarly suppressed feed consumption by 0.15 g and the average number of clicks by 304.75. A rise in water temperature stimulated shrimp behaviors such as feeding, swimming, and foraging, while elevated concentrations of ammonia nitrogen and nitrite nitrogen significantly inhibited shrimp activity. Redundancy analysis revealed that temperature was the most prominent factor among the three environmental factors influencing shrimp feeding. This study is the first to quantify the specific effects of common environmental factors on the acoustic feeding signals and feeding behavior of P. vannamei using PAM technology. It confirms the feasibility of using PAM technology to assess shrimp feeding conditions under diverse environmental conditions and the necessity of integrating environmental monitoring modules into future feeding systems. This study provides behavioral evidence for the development of precise feeding technologies and the upgrade of intelligent feeding systems for P. vannamei. Full article

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work. Full article

Objectives: Recently, increased attention has been given to pumpkin due to its proved nutritional components, which include antioxidant, antifatigue, and anti-inflammatory effects. The aim of the present work was to assess the impact of Cucurbita pepo L. (CP) on chronic unpredictable mild stress (CUMS)-induced changes in lymphoid organs through evaluating its effect on the histological structure of spleen, thymus gland, and lymph nodes compared to the antidepressant fluoxetine (FLU). Materials and Methods: Fifty male albino rats equally distributed into five groups that included control, control + CP, CUMS-exposed, FLU-treated, and CP-treated groups were used in this study. Rats were exposed to CUMS for 4 weeks, and treatment (either with FLU or CP) was started after 14 days of exposure. Behavior of the rats, serum corticosterone, oxidants/antioxidants profile, proinflammatory cytokines, and gene expression of glucocorticoid receptor (GR) and β-adrenergic receptor (β2-AR) were assessed after 28 days. Spleen, thymus gland, and lymph nodes were histopathologically assessed. Results: CP administration significantly reduced the CUMS-induced behavioural changes evident by the significant reduction in immobility time (p = 0.02) and corticosterone level (p < 0.001). Biochemically, CP reduced TNF-α and IL-6 (p < 0.001) and markedly alleviated the changes in oxidants/antioxidants in the serum and lymphoid organs compared to fluoxetine. CP significantly (p < 0.001) reduced CUMS-induced changes in GR and (β2-AR). Histopathologically, CP alleviated changes observed in the spleen, lymph nodes, and thymus gland. It significantly reduced the number of CD4, CD8, CD68, CD20, and caspase-3 immunopositive cells in the studied organs. Conclusions: This study proved the potential efficacy of CP in alleviating depression-associated immunodysregulation either alone or in combination with antidepressant therapy. Full article

This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article

Phosphorus-solubilizing fungi represent a viable alternative to traditional fertilizers for use in coffee cultivation. The aim of this work was to select fungal consortia with a high phosphorus-solubilizing capacity for application to three varieties of coffee plants under greenhouse conditions. The research comprised three phases: Firstly, solubilizing strains were identified morphologically and molecularly. Secondly, compatibility tests were carried out to select combinations of phosphorus-solubilizing fungi. The selection of the consortia was evaluated based on their phosphorus-solubilizing capacity, and the consortia with the solubilizing activity were chosen for application to coffee plants. In the greenhouse phase, three coffee varieties were inoculated; the treatments involved single, dual, and triple inoculation, as well as a control without fungi. Five species were identified: Fusarium crassum, F. irregulare, Leptobacillium leptobactrum, Penicillium brevicompactum, and Trichoderma spirale, plus one strain of Absidia sp. The in vitro phase of the study revealed that 11 consortia demonstrated compatibility, and their phosphorus solubilization capacity and phosphatase activity were evaluated. As a result, four consortia with high phosphorus solubilization capacity were selected for inoculation on coffee plants. The greenhouse phase results showed that the three coffee varieties inoculated in consortia showed higher phosphorus availability in the substrate and significant growth. Full article

This study presents a novel approach to FTIR spectroscopy at low temperatures under atmospheric pressure. The work aimed to confirm the efficiency of a fundamentally new cryogenic setup that enables material research under the specified conditions. The new technique combines a nitrogen-based cryogenic capillary cooling system with precise temperature monitoring via a PID controller, along with DRIFT spectroscopy for hydrocarbon materials. New fundamental data were obtained on the properties and behavior of hydrocarbon compounds such as methanol, kerosene, and ethanol. The IR spectra of these samples contain key characteristic vibrations of hydrocarbon functional groups, which demonstrate the effective operability of the cryogenic device. A detailed description of the setup and measurement technique is provided, along with a thorough comparison of the results with data from other authors. The application scope of the cryogenic device, the relevance of the research, and potential future developments are also discussed. Full article

In this study, a new fibre combination for an interlayer hybrid fibre-reinforced polymer laminate was investigated to achieve pseudo-ductile behaviour in tensile tests. The chosen high-strain fibre for this purpose was S-Glass, and the low-strain fibre was flax. These materials were chosen because of their relatively low environmental impact compared to carbon/carbon and carbon/glass hybrids. An analytical model was used to find an ideal combination of the two materials. With that model, the expected stress–strain relation could also be predicted analytically. The modelling was based on preliminary tensile tests of the two basic components investigated in this research: unidirectional laminates reinforced with either flax fibres or S-Glass fibres. Hybrid specimens were then designed, produced in a heat-assisted pressing process, and subjected to tensile tests. The strain measurement was performed using distributed fibre optic sensing. Ultimately, it was possible to obtain repeatable pseudo-ductile stress–strain behaviour with the chosen hybrid when the specimens were subjected to quasi-static uniaxial tension in the direction of the fibres. The intended damage-mode, consisting of a controlled delamination at the flax-fibre/glass-fibre interface after the flax fibres failed, followed by a load transfer to the glass fibre layers, was successfully achieved. The pseudo-ductile strain averaged 0.52% with a standard deviation of 0.09%, and the average load reserve after delamination was 145.5 MPa with a standard deviation of 48.5 MPa. The integrated fibre optic sensors allowed us to monitor and verify the damage process with increasing strain and load. Finally, the analytical model was compared to the measurements and was partially modified by neglecting the Weibull strength distribution of the high-strain material. Full article

Lymphoma continues to pose a significant global health burden, highlighting the urgent need for novel therapeutic strategies. Recent advances in microbiome research have identified gut-microbiota-derived metabolites, or postbiotics, as promising candidates in cancer therapy. This study investigates the antiproliferative and mechanistic effects of two postbiotics, Nisin (N) and Urolithin B (UB), individually and in combination, against the human lymphoma cell line HKB-11. Moreover, this study evaluated cytotoxic efficacy and underlying molecular pathways using a comprehensive experimental approach, including the Alamar Blue assay, combination index (CI) analysis, flow cytometry, reactive oxygen species (ROS) quantification, and bottom-up proteomics. N and UB displayed notable antiproliferative effects, with IC₅₀ values of 1467 µM and 87.56 µM, respectively. Importantly, their combination at a 4:6 ratio demonstrated strong synergy (CI = 0.09 at IC₉₅), significantly enhancing apoptosis (p ≤ 0.0001) and modulating oxidative stress. Proteomic profiling revealed significant regulation of key proteins related to lipid metabolism, mitochondrial function, cell cycle control, and apoptosis, including upregulation of COX6C (Log₂FC = 2.07) and downregulation of CDK4 (Log₂FC = −1.26). These findings provide mechanistic insights and underscore the translational potential of postbiotics in lymphoma treatment. Further preclinical and clinical investigations are warranted to explore their role in therapeutic regimens. Full article

Ruminant livestock contribute significantly to methane emissions, necessitating sustainable mitigation strategies. Essential oils (EOs) show promise for modulating ruminal fermentation, but their synergistic effects remain underexplored. Two 24 h in vitro experiments evaluated the synergistic effects of EO blends on rumen microbial fermentation. Exp. 1 screened five oils using two triad combinations. Triad 1 tested 10 combinations of thyme (THY), peppermint (PPM), and cinnamon leaf (CIN) oils. Triad 2 tested 10 combinations of anise (ANI), clove leaf (CLO), and peppermint (PPM) oils. Each blend was tested at 400 mg/L, using batch culture methods measuring: pH, ammonia-N (NH₃-N), and volatile fatty acids (VFAs). The two most effective blends, designated as T1 and T2, were selected for Exp. 2 to assess total gas and methane (CH₄) production using pressure transducer methods. All treatments were incubated in a rumen fluid–buffer mix with a 50:50 forage-to-concentrate substrate (pH 6.6). In Exp. 1, data were analyzed according to the Simplex Centroid Design using R-Studio. In Exp. 2, an analysis was conducted using the MIXED procedure in SAS. Mean comparisons were assessed through Tukey’s test. The results from Exp. 1 identified CIN+PPM (80:20) and ANI+CLO (80:20) as optimal combinations, both increasing total VFAs while reducing acetate/propionate ratios and NH₃-N concentrations. In Exp. 2, both combinations significantly reduced total gas and CH₄ productions compared to the control, with CIN+PPM achieving the greatest methane reduction (similar to monensin, the positive control). Specific essential oil combinations demonstrated synergistic effects in modulating rumen fermentation and reducing methane emissions, offering potential for sustainable livestock production. Further in vivo validation is required to optimize dosing and assess long-term effects on animal performance. Full article

Background: Pulmonary thromboembolism (PTE) is a preventable yet potentially fatal condition with significant morbidity and mortality. Several clinical scoring systems, including the Wells and modified Geneva scores, have been developed to assess the likelihood of PTE and guide further diagnostic evaluation. The Padua prediction score, primarily used to assess venous thromboembolism (VTE) risk in hospitalized patients, has also been considered for its potential utility in suspected PTE cases. Methods: This retrospective study included 257 patients with suspected acute PTE. Diagnosis was confirmed by computed tomography pulmonary angiography (CTPA) in 140 patients (patient group), while 117 patients without radiologic evidence of PTE served as controls. All participants were evaluated using Wells, modified Geneva, and Padua scores. Sensitivity, specificity, predictive values, and the effect of combining scores with age-adjusted D-dimer levels were analyzed. Results: The Wells score demonstrated a sensitivity of 60% and specificity of 91%, with a positive predictive value of 88%. Modified Geneva and Padua scores showed lower diagnostic accuracy. Negative predictive values increased significantly when combined with age adjusted D-dimer levels. Conclusions: The Wells score was the most reliable tool among the three for predicting PTE. Combining clinical scoring with D-dimer testing enhances diagnostic accuracy and may reduce unnecessary imaging in patients with low to moderate risk. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article