Search Results (1,161)

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

Background: Myocardial scar and fibrosis predict adverse cardiac outcomes. Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is the reference standard for detection. However, it requires gadolinium-based contrast agents (GBCAs), which may be unsuitable for some patients. Cine balanced steady-state free precession (bSSFP) sequences are universally acquired in routine CMR. They may enable contrast-free scar detection via radiomics analysis. Aim: To systematically review the diagnostic accuracy of cine CMR radiomics for myocardial scar or fibrosis detection. The reference standard is visual or threshold-based LGE. Methods: This review followed PRISMA guidelines and was registered in PROSPERO (CRD420251121699). We searched MEDLINE, Embase, and Cochrane Library up to 8 August 2025. Eligible studies compared cine CMR radiomics with LGE-based assessment in patients with suspected or known scar/fibrosis. Quality was assessed using QUADAS-2 and Radiomics Quality Score (RQS). Results: Five retrospective studies (n = 1484) were included. Two focused on myocardial infarction, two on hypertrophic cardiomyopathy, and one on ischaemic versus dilated cardiomyopathy. Diagnostic performance was good to excellent (AUC 0.74–0.96). Methodological heterogeneity was substantial in reference standards, segmentation, preprocessing, feature selection, and modelling. Only one study used external validation. QUADAS-2 showed high bias risk in patient selection and index test domains. RQS scores were low (30–42%), indicating limited reproducibility and validation. Conclusions: Cine CMR radiomics shows promise as a non-contrast alternative for detecting myocardial scar and fibrosis. However, methodological standardisation, multicentre validation, and prospective studies are needed before clinical adoption. Full article

Formation damage caused by wellbore fluids remains a key concern in carbonate reservoirs, where pore plugging and filtrate invasion can severely reduce permeability. This study investigates the influence of filtrate-control components in cellulose-based polymeric fluids on the potential for formation damage in carbonate rocks and evaluates the performance of HPA starch as an alternative to cellulose, focusing on its comparative effects on formation permeability. Experimental tests were performed using Indiana Limestone cores to measure filtration behavior and permeability recovery after exposure to different polymeric solutions. The results revealed distinct mechanisms associated with each additive: PAC LV controlled fluid loss mainly by adsorption and pore plugging, while HPA starch formed more deformable and permeable structures. Glycerin, when used alone, did not induce formation damage but increased fluid viscosity, favoring more stable dispersion of the polymeric phase. Micronized calcite enhanced external cake consolidation through particle bridging. The combined use of PAC LV, glycerin, and calcite provided the most efficient filtration control and minimized formation damage. These findings contribute to understanding the isolated and synergistic roles of filtrate-control agents and support the design of optimized polymer-based fluids for well intervention and abandonment operations. Full article

Fabricating breast tumor models that mimic the natural breast tissue-like microenvironment (normal or cancerous) both physically and bio-metabolically, despite extended research, is still a challenge. A native-mimicking breast tumor model is the demand since complex biophysiological mechanisms in the native breast tissue hinder deciphering the root causes of cancer initiation and progression. Hydrogels, which mimic the natural extracellular matrix (ECM), are increasingly demanded for various biomedical applications, including tissue engineering and tumor modeling. Their biomimetic 3D network structures have demonstrated significant potential to enhance the breast tumor model, treatment, and recovery. Additionally, 3D tumor organoids cultivated within hydrogels maintain the physical and genetic traits of native tumors, offering valuable platforms for personalized medicine and therapy response evaluation. Hydrogels are broadly classified into static and dynamic hydrogels. Static hydrogels, however, are inert to external stimuli and do not actively participate in biological processes or provide scaffolding systems. Dynamic hydrogels, on the other hand, adapt and respond to the surrounding microenvironment or even create new microenvironments according to physiological cues. Dynamic hydrogels typically involve reversible molecular interactions—through covalent or non-covalent bonds—enabling the fabrication of hydrogels tailored to meet the mechanical and physiological properties of target tissues. Although both static and dynamic hydrogels can be advanced by incorporating active nanomaterials, their combinations with dynamic hydrogels provide enhanced functionalities compared to static hydrogels. Further, engineered hydrogels with adipogenic and angiogenic properties support tissue integration and regeneration. Hydrogels also serve as efficient delivery systems for chemotherapeutic and immunotherapeutic agents, enabling localized, sustained release at tumor sites. This approach enhances therapeutic efficacy while minimizing systemic side effects, supporting ongoing research into hydrogel-based breast cancer therapies and reconstructive solutions. This review summarizes the roles of dynamic hydrogels in breast tumor models. Furthermore, this paper discusses the advantages of integrating nanoparticles with dynamic hydrogels for drug delivery, cancer treatment, and other biomedical applications, alongside the challenges and future perspectives. Full article

Methicillin-resistant S. aureus (MRSA) possesses broad resistance, biofilm formation, and high virulence characteristics. Therefore, developing new therapeutic strategies against this pathogen is urgent. This work reports kaempferol (kol) and kaempferin (kin) bound to the active site of β-lactamase and interacting with key residues, thereby inhibiting its activity. In addition, kol and kin reduced the secretion of β-lactamase to the external environment, then the shielding effect of β-lactamase to β-lactam antibiotics was weakened, and finally, the bactericidal ability of ampicillin (Amp) to MRSA was enhanced. Kol and kin relieved the inflammatory responses of J774 cells induced by MRSA and improved the survival of Galleria mellonella (G. mellonella) infected by MRSA when combined with or without Amp. These data suggest that kol and kin have the potential to be developed as anti-MRSA infection agents, which would broaden the application prospects of these compounds. Full article

The method addresses the challenges of non-steady conditions at an early age by combining wavelet filtering and empirical mode decomposition (EMD) to separate strain components arising from shrinkage, expansive agent compensation, temperature variations, construction disturbances, and live loads. The approach incorporates construction logs as external constraints to ensure accurate correspondence between signal features and physical events. Scientifically, this study addresses the fundamental problem of identifying and quantifying multi-source strain components under transient and non-steady construction conditions, which remains a major challenge in the field of structural monitoring. Field monitoring was conducted on typical cast-in-place concrete components: a full-width bridge deck in the negative moment region. The results show that both structural types exhibit a distinct shrinkage–recovery process at an early age but differ in amplitude distribution, recovery rate, and restraint characteristics. During the construction procedure stage, the cast-in-place segment in the negative moment region was sensitive to prestressing and adjacent segment construction. Under variable loads, the former showed higher live load sensitivity, while the latter exhibited more pronounced temperature-driven responses. Total strain decomposition revealed that temperature and dead load were the primary long-term components in the structure, with differing proportional contributions. Representative strain variations observed in the field ranged from 10 to 50 µε during early-age shrinkage–expansion cycles to 80–100 µε reductions during prestressing operations, quantitatively illustrating the evolution characteristics captured by the proposed method. This approach demonstrates the method’s capability to reveal transient stress mechanisms that conventional steady-state analyses cannot capture, providing a reliable basis for strain monitoring, disturbance identification, and performance evaluation during construction, as well as for long-term prediction and optimization of operation–maintenance strategies. Full article

Background: Leishmania amazonensis, one of the causative agents of cutaneous leishmaniasis, is responsible for a neglected tropical disease affecting nearly one million individuals worldwide. Although clinical treatments are available, their effectiveness is often compromised by high toxicity and limited selectivity. Methods: From a database, 64 chalcone derivatives were studied using Comparative Molecular Similarity Indices Analysis (CoMSIA) and Hansch analysis (2D-QSAR) to construct predictive computational models. Internal and external validation criteria were applied to identify structural determinants associated with antileishmanial activity. Based on these insights, twelve novel quinoline–chalcone hybrids were designed, synthesized, and biologically evaluated against L. amazonensis. Results: The most robust CoMSIA model combined steric and hydrogen-bond acceptor fields (CoMSIA-SA), while Hansch analysis highlighted electronic descriptors—specifically LUMO energy and the global electrophilicity index—as critical for parasite growth inhibition. Guided by these computational findings, a new series of 12 hybrid quinoline–chalcone derivatives (E001–E012) was synthesized through a two-step procedure, achieving overall yields of 43–71%. Biological assays demonstrated that four compounds displayed inhibitory activity comparable to amphotericin B. Furthermore, physicochemical profiling and in silico pharmacokinetic predictions for the most active compounds (E003, E005, E006, and E011) indicated favorable biocompatibility and drug-like properties. Conclusions: These results underscore the value of an integrative computational–experimental approach in the rational design of next-generation L. amazonensis inhibitors, reinforcing chalcone-based scaffolds as promising pharmacophoric frameworks for antileishmanial drug discovery. Full article

In the US and Europe, institutions, foundations and governments invest significant financial resources in doctoral fellowships. Unlike other graduate funding mechanisms, fellowships are typically not tied to specific projects or job responsibilities and thus may afford more agency to students. We examined how fellowship funding contributes to or undermines agency of doctoral student recipients. We interviewed 23 US engineering doctoral students primarily funded on a fellowship for at least one semester. We qualitatively analyzed the interviews, using inductive and deductive methods of coding. Participants described increased flexibility with their projects, advisor, and personal life; additional access to physical resources, people and networks, and research experiences; and feelings of internal validation and external recognition from fellowship awards. Contexts of advising, timing of fellowship, source of fellowship, financial circumstances, and fellowship structure influenced their experiences. Agentic perspectives and actions included choice of advisor and research projects, switching advisors if necessary, completing internships and visiting other labs, and enjoying a higher standard of living. Advisor support is a necessity for students funded on fellowships. Multi-year fellowships from external sources, in comparison to internal sources, more often supported agency. We make recommendations for institutions to structure and administer fellowships to better support students. Full article

Barium carbonate (BaCO₃) nanoparticles were synthesized by a facile hydrolysis route using BaCl₂ and KOH in aqueous solution, with atmospheric CO₂ as the carbonate source, without external agents. Their structural and morphological properties were investigated by XRD, ATR-FTIR, SEM, and BET, confirming the formation of a pure orthorhombic witherite phase with rod-like morphology and different surface specific areas. The crystallite size increased from 52 to 86 nm with higher precursor concentration and synthesis temperature, as predicted by a regression model correlating synthesis parameter with particle growth. When incorporated into polymer (PVC) and geopolymer (GP) matrices, BaCO₃ enhanced the photocatalytic degradation of methylene blue (MB) under solar light, with GP@Nano-BaCO₃ achieving a higher rate constant compared to PVC@Nano-BaCO₃. The results highlight that the synthesis strategy yields well-defined BaCO₃ nanoparticles with tunable structural features and promising photocatalytic potential when integrated in functional polymer matrices. Future work will address doping strategies and testing in real wastewater conditions. Overall, this synthesis strategy offers a reproducible and environmentally friendly route to BaCO₃ nanoparticles with potential applications in hybrid materials for visible light-driven environmental remediation. Full article

This paper presents a hybrid reinforcement learning framework for trajectory tracking control of a 5-degree-of-freedom (DOF) Mitsubishi RV-2AJ robotic arm by integrating model-free deep reinforcement learning (DRL) algorithms with classical control strategies. A novel hybrid PID + TD3 agent is proposed, combining a Proportional–Integral–Derivative (PID) controller with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and is compared against standalone TD3 and PID controllers. In this architecture, the PID controller provides baseline stability and deterministic disturbance rejection, while the TD3 agent learns residual corrections to enhance tracking accuracy, robustness, and control smoothness. The robotic system is modeled in MATLAB/Simulink with Simscape Multibody, and the agents are trained using a reward function inspired by artificial potential fields, promoting energy-efficient and precise motion. Extensive simulations are performed under internal disturbances (e.g., joint friction variations, payload changes) and external disturbances (e.g., unexpected forces, environmental interactions). Results demonstrate that the hybrid PID + TD3 approach outperforms both standalone TD3 and PID controllers in convergence speed, tracking precision, and disturbance rejection. This study highlights the effectiveness of combining reinforcement learning with classical control for intelligent, robust, and resilient robotic manipulation in uncertain environments. Full article

Background: The tumor microenvironment offers a new perspective in gynecologic oncology. In ovarian cancer, numerous preclinical studies, especially organoid models, have highlighted cellular, immune, and biochemical mechanisms. Beyond these sophisticated findings, more practical aspects require attention, such as the role of vaginal microbiota, which represents an interplay between external agents and internal genitalia, and its potential profiling role in early detection beyond the promise of microbiota-targeted therapies. Objectives: This review aims to assess whether such a correlation is speculative or scientifically grounded. Methods: A focused literature search was conducted on vaginal microbiota and its correlation with ovarian cancer to define the current state of knowledge. Results: Mixed outcomes have been reported, yet there is a rational and scientific basis supporting further investigation. Clinical approaches increasingly consider vaginal microbiota as relevant. However, we have to say that most available evidence is still preliminary and largely preclinical to set realistic expectations for readers. Although additional studies are needed, emerging insights highlight its importance and practical implications. We present a diagnostic–therapeutic management flowchart summarizing current evidence). Discussion: Most links between the vaginal microbiota and ovarian cancer are correlational rather than causal. The idea that microbes ascend from the vagina to the ovaries is proposed but still definitely not demonstrated. Confounding factors like age, hormones, and BRCA status complicate interpretation, and ovarian cancer itself could secondarily alter the microbiota. Mechanistic studies and longitudinal data are still needed to clarify whether dysbiosis contributes to carcinogenesis or is merely a consequence. As gynecologists, we summarize key aspects and emphasize to colleagues the importance of incorporating these findings into daily clinical practice. Vaginal dysbiosis should be considered not only a local imbalance but also a potential strategy for primary cancer prevention. Conclusions: Future research on the tumor microenvironment and vaginal microbiota will expand scientific knowledge and guide innovative preventive and therapeutic strategies. Full article

Games are considered a suitable and standard benchmark for checking the performance of artificial intelligence-based algorithms in terms of training, evaluating, and comparing the performance of AI agents. In this research, an application of the Intrinsic Curiosity Module (ICM) and the Asynchronous Advantage Actor–Critic (A3C) algorithm is explored using action games. Having been proven successful in several gaming environments, its effectiveness in action games is rarely explored. Providing efficient learning and adaptation facilities, this research aims to assess whether integrating ICM with A3C promotes curiosity-driven explorations and adaptive learning in action games. Using the MAME Toolkit library, we interface with the game environments, preprocess game screens to focus on relevant visual elements, and create diverse game episodes for training. The A3C policy is optimized using the Proximal Policy Optimization (PPO) algorithm with tuned hyperparameters. Comparisons are made with baseline methods, including vanilla A3C, ICM with pixel-based predictions, and state-of-the-art exploration techniques. Additionally, we evaluate the agent’s generalization capability in separate environments. The results demonstrate that ICM and A3C effectively promote curiosity-driven exploration in action games, with the agent learning exploration behaviors without relying solely on external rewards. Notably, we also observed an improved efficiency and learning speed compared to baseline approaches. This research contributes to curiosity-driven exploration in reinforcement learning-based virtual environments and provides insights into the exploration of complex action games. Successfully applying ICM and A3C in action games presents exciting opportunities for adaptive learning and efficient exploration in challenging real-world environments. Full article

Agriculture is a major economic industry that sustains life. Moreover, plant health is a crucial aspect of a highly functional agricultural system. Because stress agents can damage crops and plants, it is important to understand what effect these agents can have and be able to detect this negative impact early in the process. Machine learning technology can help to prevent these undesirable consequences. This research investigates machine learning applications for plant health analysis and classification. Specifically, Residual Networks (ResNet) and Long Short-Term Memory (LSTM) models are utilized to detect and classify plants response to abiotic external stressors. Two types of plants, azalea (shrub) and Chinese tallow (tree), were used in this research study and different concentrations of sodium chloride (NaCL) and acetic acid were used to treat the plants. Data from cameras and soil sensors were analyzed by the machine learning algorithms. The ResNet34 and LSTM models achieved accuracies of 96% and 97.8%, respectively, in classifying plants with good, medium, or bad health status on test data sets. These results demonstrate that machine learning algorithms can be used to accurately detect plant health status as well as healthy and unhealthy plant conditions and thus potentially prevent negative long-term effects in agriculture. Full article

This paper shows that a common form of external habit persistence, despite having much success in asset pricing, implies an extreme degree of conformity in investors’ portfolio choice. If an investor with this utility function uses US aggregate consumption as her external habit benchmark, she has to hold all non-redundant securities contained in the US aggregate wealth portfolio. Even for an investor who uses the average consumption of a more narrowly-defined community as her benchmark, she is still required to hold non-zero positions in all (non-redundant) individual stocks held by any other member of the community. If markets are incomplete, even if an individual investor holds a financial portfolio that conforms perfectly with that associated with the external habit benchmark, it is still impossible for the investor to ensure that consumption exceeds habit in all states of the world. Because of this implication, this form of external habit is unlikely to describe the preferences of individual investors—notwithstanding its success as a model for the representative agent in asset pricing. Full article

This study considered and compared silver, gold, and their combination of nanoparticles (AgNPs, AuNPs, and Au-AgNPs) with biocompatible material mesoporous silica SBA-15 as potential antibacterial agents. A facile, one-pot “green” methodology, utilizing L-histidine as a reducing agent and bridge between components, was employed to obtain Ag@SBA-15, Au@SBA-15, and Au-Ag@SBA-15 nanocomposites without the use of external additives. Various physicochemical tools (UV-Vis, TEM, SAED, FESEM, XPS, BET, XRD, and FTIR) presented SBA-15 as a good carrier for spherical AgNPs, AuNPs, and Au-AgNPs with average diameters of 8.5, 16, and 9 nm, respectively. Antibacterial evaluations of Escherichia coli and Staphylococcus aureus showed that only Ag@SBA-15, at a very low Ag concentration (1 ppm) during 2 h of contact, completely reduced the growth (99.99%) of both strains, while the Au@SBA-15 nanocomposite required higher concentrations (5 ppm) and time (4 h) to reduce 99.98% E. coli and 94.54% S. aureus. However, Au introduction in Ag@SBA-15 to form Au-Ag@SBA-15 negatively affected its antibacterial potential, lowering it due to the galvanic replacement reaction. Nevertheless, the rapid and effective combating of two bacteria at low NPs concentrations, through the synergistic effects of mesoporous silica and AgNPs or AuNPs, in Ag@SBA-15 and Au@SBA-15 nanocomposites, provides a potential substitute for existing bacterial disinfectants. Full article