Search Results (24,388)

The polymer industry gained increasing importance due to the ability of polymers to replace traditional materials such as wood, glass, and metals in various applications, offering advantages such as high strength-to-weight ratio, corrosion resistance, and ease of fabrication. Among key performance indicators, melt flow rate (MFR) plays a crucial role in determining polymer quality and processability. However, conventional offline laboratory methods for measuring MFR are time-consuming and unsuitable for real-time quality control in industrial settings. To address this challenge, the study proposes a leveraging artificial intelligence with machine learning-based melt flow rate prediction for polymer properties analysis (LAIML-MFRPPPA) model. A dataset of 1044 polymer samples was used, incorporating six input features such as reactor temperature, pressure, hydrogen-to-propylene ratio, and catalyst feed rate, with MFR as the target variable. The input features were normalized using min–max scaling. Two ensemble models—kernel extreme learning machine (KELM) and random vector functional link (RVFL)—were developed and optimized using the pelican optimization algorithm (POA) for improved predictive accuracy. The proposed method outperformed traditional and deep learning models, achieving an R² of 0.965, MAE of 0.09, RMSE of 0.12, and MAPE of 3.4%. A SHAP-based sensitivity analysis was conducted to interpret the influence of input features, confirming the dominance of melt temperature and molecular weight. Overall, the LAIML-MFRPPPA model offers a robust, accurate, and deployable solution for real-time polymer quality monitoring in manufacturing environments. Full article

The growing reliance on unconventional water sources requires effective strategies for forage production, particularly in semiarid regions. The objective of this study was to evaluate evapotranspiration, phytomass accumulation, and salt inputs to the soil in millet crops irrigated with wastewater under different soil water levels, both with and without organic fertilizer. We conducted the experiment in a protected environment using a randomized block design in a 4 × 2 + 1 (control) factorial scheme with three replicates. We applied four irrigation levels with wastewater (25, 50, 75, and 100% of available soil water) with or without organic fertilizer. The control treatment was watering with the public supply submitted to 100% of the available water from the soil without fertilization. Wastewater did not affect biomass without fertilization, but irrigation levels significantly influenced productivity under fertilization. Additionally, applying 50% of available soil water proved the most efficient system in terms of yield per unit of water consumed. Although irrigation improved productivity, water use efficiency in millet showed limited enhancement. The millet did not exhibit any symptoms of salt stress. Finally, we emphasize caution when using wastewater (graywater) for irrigation, as continuous application can lead to salt accumulation and subsequent soil salinization. Full article

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice. Full article

Understanding the relationship between work stress and job satisfaction is crucial for promoting employee well-being and also for sustainable organizational performance. This study proposes and validates, within the population of employees in Serbia, the Stress Symptoms Survey (SSS), an 18-item instrument for measuring physical and psychological symptoms of work-related stress. The scale shows strong internal consistency where a general factor is highly saturated with all survey items. Regression analysis indicated that lack of organizational support was the strongest predictor of lowered job satisfaction, followed by stress symptoms and general job stress; job pressure showed a positive association with job satisfaction when other stressors were controlled. These results highlight the practical value of the SSS and underscore the crucial role of supportive work environments in mitigating stress, enhancing satisfaction and achieving sustainable work performance. Full article

Low-temperature environments in cold regions pose a significant threat to cattle farming. Bovine mammary epithelial cells (BMECs) are highly sensitive to cold stress, and acute cold stress can induce apoptosis, adversely affecting lactation performance and health. To explore the mechanism of acute cold stress-induced apoptosis in BMECs, we established an in vitro acute cold stress model. Results showed that mRNA levels of HSP90 increased significantly in a time-dependent manner after 2 h of cold stress, confirming successful model establishment. Following 4 h of cold stress, pro-apoptotic genes (Caspase-3, Bax) exhibited significantly elevated mRNA levels, while the anti-apoptotic gene (BCL-2) showed significantly reduced mRNA levels. Concurrently, the apoptosis rate increased significantly, indicating that acute cold stress induces apoptosis and suggesting the 4 h mark may represent a critical transition point. Integrated transcriptomic and functional analyses identified ENO1 as a core metabolic regulator counteracting acute cold stress-induced apoptosis in BMECs. As a multifunctional protein, ENO1 (alpha-enolase) acts as a central enzyme in glycolysis while exerting additional roles in cellular signaling and apoptotic processes, thereby participating in various pathophysiological regulations. Both mRNA and protein levels of ENO1 were significantly elevated in cold-stressed cells compared to untreated controls. Importantly, interference with ENO1 expression aggravated the extent of cold stress-induced apoptosis, demonstrating the regulatory role of ENO1 in this process. To our knowledge, this is the first report elucidating the core regulatory function of ENO1 in acute cold stress-induced apoptosis in BMECs. These findings provide a theoretical basis for understanding apoptotic mechanisms under stress. Full article

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article

As wind energy development continues to expand toward nearshore and deep-sea regions, enhancing the aerodynamic efficiency of vertical axis wind turbines (VAWTs) in complex marine environments has become a critical challenge. To address this, a composite flow control strategy combining leading-edge suction and trailing-edge gurney flap is proposed. A two-dimensional unsteady numerical simulation framework is established based on CFD and the four-equation Transition SST (TSST) transition model. The key control parameters, including the suction slot position and width as well as the gurney flap height and width, are systematically optimized through orthogonal experimental design. The aerodynamic performance under single (suction or gurney flap) and composite control schemes is comprehensively evaluated. Results show that leading-edge suction effectively delays flow separation, while the gurney flap improves aerodynamic characteristics in the downwind region. Their synergistic effect significantly suppresses blade load fluctuations and enhances the wake structure, thereby improving wind energy capture. Compared to all other configurations, including suction-only and gurney flap-only blades, the composite control blade achieves the most significant increase in power coefficient across the entire tip speed ratio range, with an average improvement of 67.24%, demonstrating superior aerodynamic stability and strong potential for offshore applications. Full article

Background/Objectives: Following the recent publication of reassuring outcomes from the ARA MED 330 protocol regarding long-term insulin use in pilots, combined with continuous advancements in diabetes technology, European aeromedical examiners are increasingly optimistic about establishing more flexible medical requirements for insulin-treated aviation professionals. These professionals have historically been considered unfit for duty due to hypoglycemic risks. According to current research, hypoglycemia, the primary incapacitation risk for flight crew, is considered virtually non-existent among air traffic controllers (ATCOs). Additionally, stress-induced hyperglycemia also represents a low-frequency risk in these professionals, who are experienced in managing highly stressful operational environments. This study presents a narrative review examining stress and its metabolic effects in healthy individuals, ATCOs, and people with diabetes (PwD). Methods: This narrative review was conducted based on a comprehensive PubMed search performed by two independent reviewers (GAR and AM) spanning January 2023 to January 2025. The search strategy focused on English-language, peer-reviewed studies involving human participants and addressed stress, glucose regulation, and occupational factors in ATCOs and people with diabetes. Additional relevant articles were identified through reference screening. A total of 33 studies met the inclusion criteria. Studies focusing solely on oxidative or molecular mechanisms were excluded from the analysis. Results: Stressful events consistently triggered the expected hyperglycemic reaction in both healthy individuals and PwD. However, the literature indicates ATCOs demonstrate remarkable stress resilience and adaptation to the demanding conditions of their work environment, suggesting a unique occupational profile regarding metabolic stress responses. Conclusions: These findings contribute valuable insights to ongoing discussions regarding aeromedical fitness standards. The evidence suggests that ATCOs may not face the same metabolic risks as flight crews, indicating that current medical certification processes for insulin-treated aviation professionals warrant reconsideration in light of this emerging evidence. This research supports the potential for more individualized, occupation-specific aeromedical standards that better reflect the actual risk profiles of different aviation roles. Full article

Metabolism is a tightly controlled, but plastic network of pathways that allow cells to grow and maintain homeostasis. As a normal cell transforms into a malignant cancer cell and proliferates to establish a tumor, it utilizes a variety of metabolic pathways that support growth, proliferation, and survival. Cancer cells alter metabolic pathways in different contexts, leading to complex metabolic heterogeneity within a tumor. There is an unmet need to characterize how cancer cells alter how they use resources from the environment to evolve, spread to other sites of the body, and survive current standard-of-care therapies. We review key techniques and methods that are currently used to study cancer metabolism and provide drawbacks and considerations in using one over another. The goal of this review is to provide a methods’ guide to study different aspects of cell and tissue metabolism, how they can be applied to cancer, and discuss future perspectives on advancements in these areas. Full article

Background: Borderline Personality Disorder (BPD) is a complex psychiatric condition with multifactorial origins, with a high proportion of patients reporting early trauma. Stressors such as adverse childhood experiences (ACEs) can shape the epigenetic landscape including DNA methylation (DNAm) and act on gene expression. DNAm is increasingly being investigated as a molecular link between environmental exposures such as ACE and psychiatric outcomes. Differential DNAm of the gene PR domain zinc finger protein 8 (PRDM8), a histone methyltransferase, has recently been reported to be sensitive to early life trauma. Its role in BPD, especially in the context of ACE, remains to be elucidated. Methods: This study investigated DNAm patterns of PRDM8 in peripheral blood and saliva obtained from BPD patients undergoing Dialectic Behavioral Therapy (DBT) compared to healthy control (HC) participants. Associations with ACE and BPD symptom severity were assessed, and therapy-related changes in DNAm were examined. Results: At baseline, BPD patients demonstrated significant hypomethylation of PRDM8 in blood relative to the HC group. Following DBT, a nominally significant increase in DNAm was observed, aligning with inversely correlated symptom severity. No significant differences in saliva were detected. ACE was not associated with PRDM8 DNAm. Conclusions: Our findings suggest that PRDM8 DNAm might be associated with BPD and therapeutic intervention but not with ACE. Together with prior research, the results underscore the importance of future investigation of gene–environment interactions and the functional significance of PRDM8 regulation in the pathophysiology of BPD. Full article

The genus Sclerodermus Latreille (Hymenoptera: Bethylidae) comprises over 80 species of ectoparasitoids of insect pests in forests, agricultural environments, and stored products with a cosmopolitan distribution. Despite its growing significance in biological control, behavioral ecology, and public health, the taxonomy of the genus remains poorly resolved. This is largely due to morphological reduction and simplification among species, outdated or incomplete original descriptions, and limited access to type material. A particularly problematic case is Sclerodermus cereicollis Kieffer, originally described from two geographically disjunct populations: Giglio Island (Italy, Palaearctic) and Annobón Island (Equatorial Guinea, Afrotropical). The syntype series includes morphologically divergent specimens, casting doubt on their conspecificity. In this study, we redescribe S. cereicollis based on both the original syntypes and newly collected material from Italy. A lectotype is designated to stabilize the nomenclature, and we provide the first molecular data for the species to assess genetic cohesion among populations. Comparative morphological and molecular analyses reveal that the Afrotropical syntypes represent a distinct, previously undescribed species. Accordingly, we describe Sclerodermus annobonensis Masini, Colombo & Azevedo sp. nov., designating a holotype. This study refines species boundaries within Sclerodermus and highlights the value of integrative taxonomy, combining historical and contemporary data, in resolving persistent systematic ambiguities in morphologically conservative taxa. Full article

In recent years, research into the synthesis of innovative biomaterials for prosthetic applications has been increasingly growing. In particular, there is a demand for biomaterials with an excellent biocompatibility that can interact with biological fluids. This study involved the development of new silica (SiO₂)-based composite materials using the sol–gel technique and functionalization with ferulic acid (FA), a natural phenolic compound renowned for its biological properties. The synthesis involved controlling the hydrolysis and condensation of tetraethyl orthosilicate (TEOS) in acidic and alcoholic environments to incorporate ferulic acid into the sol phase matrix at different weight compositions (5, 10, 15, and 20 wt%). Fourier transform infrared spectroscopy analyses (FTIR) confirmed the successful incorporation of the bioactive compound, and in vitro tests revealed a good cytocompatibility and controlled ferulic acid release over time. These results demonstrate that the developed material shows promise as a bioactive coating for orthopedic prostheses, improving bone integration and reducing undesirable post-operative phenomena. Full article

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with $k-5$ and $k-10$ nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), Power Factor ($PF$), and Power Load ($PL$). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for $TH{D}_u$, $TH{D}_i$, and $PF$. Only BODT obtained a good forecasting result for $PL$. The KNN (k = 5) algorithm obtained a good result for $PF$ prediction. The KNN (k = 10) algorithm predicted acceptable results for U and $PF$. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees. Full article

The extremophilic strain Vreelandella titanicae Zn11_249 was isolated from Salar de Uyuni, an environment with high salinity, among other extreme factors. This study researched the optimised production, characterisation, antioxidant activity, and cytotoxicity of exopolysaccharides (EPS) produced by this strain under different ionic stresses. Zn11_249 was cultured in a minimal medium with glucose as the sole carbon source as a control, and under kosmotropic (NaCl, 1 M) and chaotropic (LiCl, 0.3 M) conditions, yielding EPS_U1, EPS_U2, and EPS_U3, respectively. Maximum EPS production (336 mg/L) occurred under chaotropic conditions after 96 h. EPSs were characterised using the following techniques: Gas chromatography (GC-MS); Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR); Thermogravimetric Analysis (TGA); and Differential Scanning Calorimetry, (DSC). The results showed differences between the molecular weights for EPS_U1 (3.9 × 10⁴ Da), EPS_U2 (3.9 × 10⁴ Da), and EPS_U3 (5.85 × 10⁴ Da). Their monosaccharide molar ratios (%) were 40/25/25/10 in EPS_U1, 10/30/30/30 in EPS_U2, and 25/25/25/25 in EPS_U3, composed of mannose, galactose, rhamnose, and glucose, respectively. Functional group analysis confirmed their heteropolysaccharide nature. Thermal profiles suggest the potential of these exopolysaccharides as biomaterials. Antioxidant tests demonstrated significant activity against DPPH, OH, and O₂ radicals, while cytotoxicity assays showed no toxicity. These results highlight the biotechnological potential of EPSs from Veelandella titanicae Zn11_249 for biomedical and cosmetic uses. Full article

As digital infrastructures become increasingly dynamic and complex, traditional static access control mechanisms are no longer sufficient to counter advanced and persistent cyber threats. In response, Zero Trust Architecture ($ZTA$) emphasizes continuous verification and context-aware access decisions. To realize these principles in practice, this study introduces a Trust Score ($TS$)-based access control model as a systematic alternative to legacy, rule-driven approaches that lack adaptability in real-time environments. The proposed $TS$ model quantifies the trustworthiness of users or devices based on four core factors—User Behavior (B), Network Environment (N), Device Status (D), and Threat History (T)—each derived from measurable operational attributes. These factors were carefully structured to reflect real-world Zero Trust environments, and a total of 20 detailed sub-metrics were developed to support their evaluation. This design enables accurate and granular trust assessment using live operational data, allowing for fine-tuned access control decisions aligned with Zero Trust principles. A comprehensive sensitivity analysis was conducted to evaluate the relative impact of each factor under different weight configurations and operational conditions. The results revealed that B and N are most influential in real-time evaluation scenarios, while B and T play a decisive role in triggering adaptive policy responses. This analysis provides a practical basis for designing and optimizing context-aware access control strategies. Empirical evaluations using the UNSW-NB15 dataset confirmed the $TS$ model’s computational efficiency and scalability. Compared to legacy access control approaches, the $TS$ model achieved significantly lower latency and higher throughput with minimal memory usage, validating its suitability for deployment in real-time, resource-constrained Zero Trust environments. Full article