Search Results (33,621)

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

Erica spiculifolia Salisb. (Balkan heath) is an evergreen shrub growing in the mountain shrublands of Eastern Europe. E. spiculifolia was used as a diuretic, anti-inflammatory, and antioxidant herbal remedy. The present study aims to conduct an evaluation of the phytochemical composition and antitumor activity of the methanol–aqueous extract from E. spiculifolia aerial parts to explore its potential in cancer treatment. Overall, a total of 54 secondary metabolites, including 28 hydroxybenzoic, hydroxycinnamic acids, and phenolic glycosides, and 10 triterpene acids, together with 17 flavonoids, were identified or annotated in the assayed E. spiculifolia extract using liquid chromatography-high-resolution mass spectrometry. The cytotoxic activity of the extract, alongside gallic, protocatechuic, and oleanolic acids as its constituents, was screened against a panel of malignant human cell lines of different origin (LAMA-84, HL-60, MDA-MB-231, MCF-7, and CASKI). The most prominent antiproliferative effect of the studied extract (with IC₅₀ 16.6 μg/mL), matched with the highest tumor selectivity (SI > 120), was observed in the LAMA-84 myeloid cells. These findings were further supported by gallic and oleanolic acid (IC₅₀ 6.2 and 1.7 μg/mL, respectively), accounting for a more distinct cytotoxicity. The strongest selective antineoplastic activity was achieved towards the triple-negative breast carcinoma cell line MDA-MB-231, with an IC₅₀ of 32.5 μg/mL. This study provided compelling evidence for a wide spectrum of E. spiculifolia antitumor activity, indicating its potential as a natural alternative for future therapeutic applications. Full article

Background: In elite football, understanding how age impacts players’ physical performance is essential for optimizing training, career longevity, and team management. Objectives: This study aimed to compare variations in physical capabilities of professional football players by chronological age and identify peak performance ages. Methods: Data from 5203 match performances across 351 official games were analyzed, involving 98 male players aged 18–39 years. Physical capacities (speed, explosive actions, and endurance) were assessed using the Catapult VECTOR7 system. Results: showed that players over 32 years experienced declines in high-intensity and explosive actions, while endurance remained relatively stable with age. Peak performance occurred around 25.7 years for speed, 24.8 years for endurance, and 26 years for explosiveness. Conclusions: Overall, players aged 17–26 years demonstrated the highest physical performance, with notable declines observed in older age groups. Full article

This paper presents a comprehensive evaluation of keypoint detection and description algorithms for stereo vision-based odometry in dynamic environments. Five widely used methods—FAST, GFTT, ORB, BRISK, and KAZE—were analyzed in terms of detection accuracy, robustness to image distortions, computational efficiency, and suitability for embedded systems. Using the KITTI dataset, the study assessed the influence of image resolution, noise, blur, and contrast variations on keypoint performance. The matching quality between stereo image pairs and across consecutive frames was also examined, with particular attention to drift—cumulative trajectory error—during motion estimation. The results show that while FAST and ORB detect the highest number of keypoints, GFTT offers the best balance between matching quality and processing time. KAZE provides high robustness but at the cost of computational load. The findings highlight the trade-offs between speed, accuracy, and resilience to environmental changes, offering practical guidance for selecting keypoint algorithms in real-time stereo visual odometry systems. The study concludes that GFTT is the most suitable method for trajectory estimation in dynamic, real-world conditions. Full article

The General Data Protection Regulation (GDPR) imposes additional demands and obligations on service providers that handle and process personal data. In this paper, we examine how advanced cryptographic techniques can be employed to develop a privacy-preserving solution for ensuring GDPR compliance in Industrial Internet of Things (IIoT) systems. The primary objective is to ensure that sensitive data from IIoT devices is encrypted and accessible only to authorized entities, in accordance with Article 32 of the GDPR. The proposed system combines Decentralized Attribute-Based Encryption (DABE) with smart contracts on a blockchain to create a decentralized way of managing access to IIoT systems. The proposed system is used in an IIoT use case where industrial sensors collect operational data that is encrypted according to DABE. The encrypted data is stored in the IPFS decentralized storage system. The access policy and IPFS hash are stored in the blockchain’s smart contracts, allowing only authorized and compliant entities to retrieve the data based on matching attributes. This decentralized system ensures that information is stored encrypted and secure until it is retrieved by legitimate entities, whose access rights are automatically enforced by smart contracts. The implementation and evaluation of the proposed system have been analyzed and discussed, showing the promising achievement of the proposed system. Full article

Exercise activates many metabolic and signaling pathways in skeletal muscle and other tissues and cells, causing numerous systemic beneficial metabolic effects. Traditionally recognized for their principal role in oxygen (O₂) transport, erythrocytes have emerged as dynamic regulators of vascular homeostasis. Beyond their respiratory function, erythrocytes modulate vascular tone through crosstalk with other cells and tissues, particularly under hypoxia and physical exercise. This regulatory capacity is primarily mediated through the controlled release in the bloodstream of adenosine triphosphate (ATP) and nitric oxide (NO), two potent vasodilators that contribute significantly to matching oxygen supply with tissue metabolic demand. Emerging evidence suggests that many other erythrocyte-released molecules may act as additional factors involved in tissue-erythrocyte crosstalk. This review highlights erythrocytes as active contributors to exercise-induced adaptations through their exocrine signaling. Full article

Unmanned aerial vehicles (UAVs) have become a tool for solving numerous practical tasks. UAV sensors provide images and videos for on-line or off-line data processing for object localization, classification, and tracking due to the use of trained convolutional neural networks (CNNs) and artificial intelligence. However, quality of images acquired by UAV-based sensors is not always perfect due to many factors. One of them could be noise arising because of several reasons. Its presence, especially if noise is intensive, can make significantly worse the performance characteristics of CNN-based techniques of object localization and classification. We analyze such degradation for a set of eleven modern CNNs for additive white Gaussian noise model and study when (for what noise intensity and for what CNN) the performance reduction becomes essential and, thus, special means to improve it become desired. Representatives of two most popular families, namely the block matching 3-dimensional (BM3D) filter and DRUNet denoiser, are employed to enhance images under condition of a priori known noise properties. It is shown that, due to preliminary denoising, the CNN performance characteristics can be significantly improved up to almost the same level as for the noise-free images without CNN retraining. Performance is analyzed using several criteria typical for image denoising, object localization and classification. Examples of object localization and classification are presented demonstrating possible object missing due to noise. Computational efficiency is also taken into account. Using a large set of test data, it is demonstrated that: (1) the best results are usually provided for SSD Mobilenet V2 and VGG16 networks; (2) the performance characteristics for cases of applying BM3D filter and DRUNet denoiser are similar but the use of DRUNet is preferable since it provides slightly better results. Full article

Personnel selection represents a two-sided matching problem in which firms compete for qualified candidates by designing job-offer packages. While traditional models assume fully rational agents, real-world decision-makers often face bounded rationality due to limited information and cognitive constraints. This study develops a matching framework that incorporates bounded rationality through the Quantal Response Equilibrium, where firms and candidates act as probabilistic rather than perfect optimizers under uncertainty. Using Maximum Likelihood Estimation and organizational hiring data, we validate that both sides display bounded rational behavior and that rationality increases as the selection process advances. Building on these findings, we propose a two-stage stochastic optimization approach to determine optimal job-offer packages that balance organizational policies with candidate competencies. The optimization problem is solved using particle swarm optimization, which efficiently explores the solution space under uncertainty. Data analysis reveals that only 23.10% of low-level hiring decisions align with rational choice predictions, compared to 64.32% for high-level positions. In our case study, bounded rationality increases package costs by 26%, while modular compensation packages can reduce costs by up to 25%. These findings highlight the cost implications of bounded rationality, the advantages of flexible offers, and the systematic behavioral differences across job levels. The framework provides theoretical contributions to matching under bounded rationality and offers practical insights to help organizations refine their personnel selection strategies and attract suitable candidates more effectively. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy and is linked to long-term metabolic and cardiovascular risks for both mother and child. Its pathophysiology includes increased generation of reactive oxygen species (ROS) and/or decreased antioxidant defenses; nonetheless, the redox dynamics between mother and fetus are still poorly understood. Our goal was to assess oxidative stress (via derivatives of reactive oxygen metabolites, d-ROMs) and antioxidant capacity (via biological antioxidant potential, BAP) in maternal, umbilical cord, and neonatal blood from women with GDM compared to normoglycemic controls, and to investigate potential associations with clinical and neonatal outcomes. Methods: In this single-center cross-sectional study, 56 women with GDM and 52 matched controls provided maternal venous, umbilical cord, and neonatal blood samples at delivery. Plasma d-ROMs and BAP were measured using colorimetric assays. Clinical and neonatal outcome data were collected. Results: Women with GDM had considerably higher maternal d-ROM levels compared to both the umbilical cord and neonatal compartments. BAP measurements revealed that maternal blood had the lowest antioxidant capacity, while cord and newborn samples had higher levels. GDM mothers had significantly greater maternal d-ROMs and lower BAP compared to controls (both p < 0.05). There were no differences in cord blood d-ROMs or BAP between the GDM and the control group. The maternal BAP/d-ROM ratio decreased significantly in the GDM group (p < 0.01), but the cord ratio remained constant. Notably, neither maternal nor neonatal redox indicators were related to perinatal outcomes, indicating a limited prognostic potential for unfavorable neonatal occurrences. Conclusions: GDM is associated with increased maternal oxidative stress and decreased antioxidant capacity, with no substantial changes in newborn redox status. Redox indicators did not predict perinatal issues across this group. These findings demonstrate the need for larger prospective research to determine whether early changes in redox balance can predict the development of GDM or unfavorable outcomes. Full article

Alopecia areata (AA) is a chronic autoimmune disorder characterized by non-scarring hair loss, with subtypes ranging from patchy alopecia (AAP) to alopecia totalis and universalis (AT/AU). The aim of this research is to investigate molecular features across AA severity by performing an integrated analysis of scalp transcriptomic datasets (GSE148346, GSE68801, GSE45512, GSE111061) and matched serum proteomic data from GSE148346. Differential expression analysis indicated that, relative to normal scalp, non-lesional AA tissue shows early immune activation—including Type 1 (C-X-C motif chemokine ligand 9 (CXCL9), CXCL10, CD8a molecule (CD8A), C-C motif chemokine ligand 5 (CCL5)) and Type 2 (CCL13, CCL18) signatures—together with reduced expression of hair-follicle structural genes (keratin 32(KRT32)–35, homeobox C13 (HOXC13)) (FDR < 0.05, |fold change| > 1.5). Lesional AAP and AT/AU scalp showed stronger pro-inflammatory upregulation and greater loss of keratins and keratin-associated proteins (KRT81, KRT83, desmoglein 4 (DSG4), KRTAP12/15) compared with non-lesional scalp (FDR < 0.05, |fold change| > 1.5). Ferroptosis-associated genes (cAMP responsive element binding protein 5 (CREB5), solute carrier family 40 member 1 (SLC40A1), (lipocalin 2) LCN2, SLC7A11) and IRS (inner root sheath) differentiation genes (KRT25, KRT27, KRT28, KRT71–KRT75, KRT81, KRT83, KRT85–86, trichohyalin (TCHH)) were consistently repressed across subtypes, with the strongest reductions in AT/AU lesions versus AAP lesions, suggesting that oxidative-stress pathways and follicular structural integrity may contribute to subtype-specific pathology. Pathway analysis of lesional versus non-lesional scalp highlighted enrichment of IFN-α/γ, cytotoxic, and IL-15 signaling. Serum proteomic profiling, contrasting AA vs. healthy controls, corroborated scalp findings, revealing parallel alterations in immune-related proteins (CXCL9–CXCL10, CD163, interleukin-16 (IL16)) and structural markers (angiopoietin 1 (ANGPT1), decorin (DCN), chitinase-3-like protein 1 (CHI3L1)) across AA subtypes. Together, these data offer an integrated view of immune, oxidative, and structural changes in AA and found ferroptosis-related and IRS genes, along with immune signatures, as potential molecular indicators to support future studies on disease subtypes and therapeutic strategies. Full article

Background: Sodium–glucose cotransporter 2 inhibitors (SGLT2i) have shown cardioprotective effects beyond glucose control. In aortic stenosis, SGLT2 expression is upregulated in myocardium and valve tissue, contributing to inflammation, oxidative stress, thrombogenicity, and calcification. SGLT2 inhibition may counteract these mechanisms, potentially reducing bioprosthetic valve failure after transcatheter aortic valve replacement (TAVR), where the diseased native valve remains in place. Objectives: This study aimed to evaluate whether SGLT2i use is associated with improved clinical outcomes, including all-cause mortality and bioprosthetic valve failure, following TAVR. Methods: We conducted a retrospective cohort study using the TriNetX global health research network. Adults with non-rheumatic aortic stenosis who underwent TAVR were stratified by SGLT2i use. Propensity score matching (1:1) was applied to balance baseline characteristics (n = 2297 per group). Primary outcomes were all-cause mortality and bioprosthetic valve failure during follow-up. Results: Before matching, SGLT2i users had more cardiovascular comorbidities. After matching, SGLT2i use was associated with a significantly lower risk of all-cause mortality (HR: 0.83; 95% CI: 0.71–0.97; p = 0.02) and bioprosthetic valve failure (HR: 0.62; 95% CI: 0.39–0.99; p = 0.04). Conclusions: In a large real-world cohort of TAVR recipients, SGLT2i use was independently associated with reduced mortality and lower risk of bioprosthetic valve failure. These findings support a potential disease-modifying role for SGLT2 inhibitors in this high-risk population and warrant further prospective investigation. Full article

Cancer-induced cardiac dysfunction has become a major clinical challenge as advances in cancer therapies continue to extend patient survival. Once regarded as a secondary concern, cardiotoxicity is now recognized as a leading contributor to morbidity and mortality among cancer patients and survivors. Its pathophysiology is multifactorial, involving systemic inflammation (e.g., TNF-α, IL-6), oxidative stress driven by reactive oxygen species (ROS), neurohormonal imbalances (e.g., angiotensin II, endothelin-1), and metabolic disturbances. These mechanisms collectively promote cardiomyocyte apoptosis, atrophy, mitochondrial dysfunction, and impaired cardiac output. Cardiac complications may arise directly from cancer itself or as adverse effects of oncologic therapies such as anthracyclines, trastuzumab, and immune checkpoint inhibitors. These agents have been linked to heart failure (HF), systolic dysfunction, and cardiac atrophy, often progressing insidiously and underscoring the importance of early detection and careful monitoring. Current preventive and therapeutic strategies include pharmacological interventions such as ACE inhibitors, beta-blockers, statins, dexrazoxane, and endothelin receptor antagonists like atrasentan. Emerging compounds, particularly Withaferin A (WFA), have shown potential through their anti-inflammatory and cardiac protective properties. In addition, antioxidants and lifestyle modifications may provide supplementary cardioprotective benefits, while interventional cardiology procedures are increasingly considered in selected patients. Despite encouraging progress, standardized treatment protocols and robust long-term outcome data remain limited. Given the heterogeneity of cancer types and cardiovascular responses, a personalized and multidisciplinary approach is essential. Continued research and close collaboration between oncologists, cardiologists, and basic scientists will be the key to advancing care, reducing treatment-related morbidity, and ensuring that improvements in cancer survival are matched by preservation of cardiovascular health. Full article

In order to establish a general design methodology for water-based electromagnetic metamaterial absorber microstructures, a topology optimization method for water-based metamaterial absorber microstructures design was proposed in this paper. According to Mie resonance and impedance matching theory, the realization mechanism and physical model of the broadband water-based metamaterial absorber were constructed. The highest average in-band absorption rate was taken as the design object; the topological optimization model for water-based metamaterial absorber design was established. A metamaterial absorber microstructure with 16 discretized water columns inside the unit cell was designed as an example. The obtained structure exhibited a very high average in band absorption rate in the specific frequency band. The proposed method was a collaborative optimization approach that employed a single type of design variable, namely water column height, to simultaneously adjust surface impedance matching and specific resonant modes. It provided a feasible method for achieving the highest average absorption rate within a specific band. Full article

Battery technology plays an increasingly vital role in portable electronic devices, electric vehicles, and renewable energy storage. During operation, batteries undergo performance degradation, which not only reduces device efficiency, but may also pose safety risks. The State of Health (SOH) is a crucial indicator for assessing battery condition. Traditional SOH prediction methods face limitations in real-time adjustment and accuracy under complex operating conditions. By determining electrode capacity loss and identifying complex patterns that traditional methods struggle to detect, prediction accuracy can be improved. Based on electrode capacity matching and compensation relationships, this paper proposes an electrode capacity balance model to evaluate battery development trends and degradation during cycling. We use q_Li − q_p state assessment as a trend criterion, q_p to quantify aging, and Q_c to identify thermal runaway risk levels, developing more efficient SOH prediction indicators and methods to ensure battery safety and performance. Full article