Search Results (147)

Wool textiles with multifunctional properties such as self-cleaning, antibacterial, electrical conductivity, UV blocking etc. have recently attracted interest. Among the materials employed towards their development, carbon nanotubes (CNTs) have been widely investigated due to their unique chemical, mechanical and electrical properties, exhibiting also notable UV-blocking properties. However, their limited dispersibility in solvents, particularly in water, has hindered their extensive industrial application and diminished their significant potential. In this work, two guanidinylated derivatives of hyperbranched polyethyleneimine (GPEI5k and PEI 25K) functionalized oxCNTs (oxCNTs@GPEI5K and oxCNTs@GPEI5K), with exceptional aqueous compatibility and colloidal stability, developed in our recent publication, were evaluated as to their antibacterial activity on Gram (-) Escherichia coli and Gram (+) Staphylococcus aureus bacteria and their cytotoxicity against mammalian cells, and the most promising, i.e., oxCNTs@GPEI5K, was subsequently used as finishing agent of wool fabric. The resulting wool textiles were evaluated for color, wash fastness, antibacterial properties, and UV-blocking performance. The GPEI-functionalized oxCNTs derivative, exhibited uniform distribution and good adhesion onto the wool fabrics yielding multifunctional wool fabrics with sustained antibacterial properties even after multiple washing cycles. Additionally, the modified textiles exhibited improved ultraviolet protection, highlighting their potential for multifunctional applications in antibacterial and UV-shielding textiles. Full article

This article explores the potential of self-directed learning (SDL) and teachers’ transformative role in promoting SDL within the educational landscape. Moreover, this article delves into the complexity of meeting varied learning demands and teachers’ dynamic obligation to provide interesting and supportive environments. This research synthesizes theoretical frameworks for SDL in educational settings. The importance of SDL is emphasized, focusing on its many benefits, including greater motivation and creativity among others. In addition, this paper makes significant academic contributions by providing a detailed analysis of the roles and features of effective teachers as self-directed learners and SDL agents. Additionally, recommendations are given for educational institutions to assist with SDL curricula implementation. These include among others providing professional development opportunities to teachers and creating appropriate learning environments. These proposed recommendations aim to bridge the gap between SDL theory and practice, paving the way for more effective application in various educational settings. Full article

The growing interest in the concept of probability of self-location of a conscious agent has created multiple controversies. Considering David Albert’s setup in which he described his worries about consistency of the concept, I identify the sources of these controversies and argue that defining “self” in an operational way provides a satisfactory meaning for the probability of self-location of an agent in a quantum world. It keeps the nontrivial feature of having subjective ignorance of self-location without ignorance about the state of the universe. It also allows defining the Born rule in the many-worlds interpretation of quantum mechanics and proving it from some natural assumptions. Full article

Interest in developing new, effective materials for emergency hemostasis and wound healing is steadily increasing, particularly for use in emergency, surgical, and military situations. Hydrogels, with their unique retention, swelling, and biocompatibility properties, have emerged as essential materials in emergency therapy. This review provides a comprehensive examination of recent hydrogel applications in acute medical scenarios, including hemostasis, wound management, drug delivery, soft tissue replacement, and tissue engineering. We discuss the physicochemical properties that make hydrogels suitable for rapid response situations, such as their tunable mechanical strength, adhesiveness, responsiveness to environmental stimuli, and ability to encapsulate and release therapeutic agents. Additionally, the article explores recent advancements in smart hydrogels with self-healing and antimicrobial properties, providing insights into their potential to revolutionize emergency care and increase survival rates in both civilian and military applications. Through a critical evaluation of current clinical trials and practical deployments, this review highlights both the successes and the challenges faced in integrating hydrogels into emergency medical protocols, providing a roadmap for future research and development in this dynamic field. Full article

Introduction. Many pharmacological properties of dipyridamole (DIP) are associated with its ability to inhibit phosphodiesterases (PDEs). Actually, DIP has interesting properties like antiviral for influenza, SARS-2 COVID-19, and herpesviruses. Our research aimed to design and synthesize the dynamic combinatorial DIP derivatives with more pronounced inhibiting properties in relation to PDE and to carry out the HPLC analysis of the resulting combinatorial derivatives of DIP. This study is aimed at investigating the effect of the dynamic derivative of dipyridamole (DDD) on intestinal dysbiosis syndrome in mice caused by streptomycin against the background of cyclophosphamide-induced cellular immunodeficiency. Materials and methods. For the synthesis of a dynamic combinatorial derivative of dipyridamole, we used a molecular dynamic method for drug design and combinatorial acylation of dipyridamole by succinic and acetic anhydride in different molar ranges of acylation agents. Combinatorial derivatives were analyzed using gradient HPLC with a UV detector. Also, derivatives established the inhibition ability for phosphodiesterase by the spectrophotometric method. Also, we used an in vivo mouse model with immunodeficiency caused by cyclophosphamide for pharmacological study. Results and discussion. Molecular modeling suggests that 18 different dipyridamole derivatives can self-assemble into a stable supramolecular structure with lower total energy. Specific combinatorial molar ratios of the synthesis components were necessary to create a new supramolecular compound with enhanced pharmacological properties. The inhibition of phosphodiesterase in such a dynamic combinatorial derivative already appeared at a concentration of 0.05 μM. In mice with colitis caused by streptomycin treatment, the administration of DDD per os resulted in an antidiarrheal effect and prevention of the animals’ weight loss. Given the cyclophosphamide-induced immunosuppression and streptomycin-associated diarrhea, immunity was completely restored only under the action of DDD. Conclusions. The most effective dipyridamole derivative for phosphodiesterase inhibition was formed only if the number of different derivatives in solution was maximum and consisted of all 18 molecules. With other quantities of modifiers, there was no qualitative change in the inhibitory activity of the combinatorial mixture against phosphodiesterase. According to all parameters, DDD has been proven to be more effective than the pure dipyridamole reference product. Full article

Background and Objectives: Contemporary caries treatment seeks to preserve hard dental tissues as well as to promote lesion remineralization and biological tissue regeneration. While fluoride-based treatments remain the gold standard, their effectiveness has limitations, prompting interest in innovative remineralization technologies. Nano-hydroxyapatite (nano-HA) varnish and self-assembling peptide (SAP) P11-4 are promising biomimetic materials that promote enamel repair, yet long-term data on their efficacy are limited. The objectives of this study were to evaluate the effectiveness of nano-HA varnish and peptide P11-4 in restoring enamel surface hardness after artificial lesions in vitro and to compare them to a control group and fluoride varnish. Materials and Methods: Artificial enamel lesions were created on the buccal surfaces of 36 extracted human molars, which were randomly divided into four groups (n = 9): control, peptide P11-4, fluoride varnish, and nano-hydroxyapatite varnish. After applying the materials as per manufacturer instructions, specimens were stored in artificial saliva for 14 days. Enamel surface hardness was measured using the Vickers hardness test (HV) at baseline, after demineralization, and after remineralization. Statistical analysis was performed with “IBM SPSS 27.0” using non-parametric Kolmogorov–Smirnov, Kruskal–Wallis, Dunn’s, and Wilcoxon tests. Results: The mean baseline enamel hardness value was 323.95 (SD 33.47) HV. After 14 days of demineralization, the mean surface hardness of artificial enamel lesions significantly plummeted to 172.17 (SD 35.96) HV (p = 0.000). After 14 days of remineralization, the mean value significantly increased to 213.21 (SD 50.58) HV (p = 0.001). The results of the study revealed statistically significant enamel remineralization of the peptide P11-4 group in regard to the demineralized enamel (p < 0.05). In contrast, there were no significant results in other treatment groups (p > 0.05). Remineralization of enamel was the highest in samples from the P11-4 group (54.1%), followed by the nano-HA group (35.4%), FV group (17.8%), and control group (11.2%). There was a significant difference (p < 0.05) in the remineralizing ability between the peptide P11-4 and all other treatment groups. Conclusions: Self-assembling peptide P11-4 effectively remineralized artificial enamel lesions and proved to be significantly more effective compared to fluoride varnish and nano-hydroxyapatite varnish, showcasing its superior performance as a remineralizing agent. Full article

Silver nanoparticles (AgNPs) have attracted considerable interest for various applications, including antiviral and antimicrobial treatments, textile nanocomposites, heat transfer and strain sensing textiles, flexible electronics, and smart textiles. Their unique properties, determined by their size, shape, and morphology, render them suitable for a wide range of uses, such as antimicrobial treatments, anticancer therapy, drug delivery, personal protective equipment (PPE), and catalysis. In this investigation, we present an in situ reactive technique for the self-assembly of AgNPs directly onto cotton yarn. A systematic investigation was undertaken to establish the influence of several synthesis parameters on the average size of AgNPs. The variables under consideration included the ambient vacuum conditions, the concentration of both Ag precursor and reducing agent, the growth temperature, and the duration of thermal treatment. By precisely optimizing these parameters, we successfully regulated the AgNPs size range between 10 and 50 nm on the cotton yarn. The findings of this study elucidate the methodology of the controlled synthesis of AgNPs on cotton yarn for potential advancements in smart textile technologies. Full article

Background/Objectives: Docetaxel is a potent anti-cancer agent capable of treating various types of cancer. However, it often induces a range of adverse reactions when used with its standard solubilizer, Tween-80, necessitating allergy prophylaxis with dexamethasone prior to administration. To mitigate the risk of allergic reactions, with nanomicelles garnering significant interest due to their enhanced solubility and thermodynamic stability. Methods: In this research, a mPEG-PLA-Lys(Fmoc) micellar carrier with m = 45 and n = 10 was engineered to encapsulate docetaxel, and its self-assembly into micelles was investigated. Additionally, allergic reaction studies were conducted on animals. Results: The findings indicated that the formulation did not cause hemolysis, vascular, or muscle irritation in rabbits, nor did it elicit an allergic response in guinea pigs. Conclusions: These results suggest that nanomicelle-encapsulated docetaxel can diminish the allergic reactions associated with docetaxel injections, offering a novel approach to enhance the therapeutic utility of this outstanding anti-cancer drug. Full article

Global fruit production costs are increasing amid intensified labor shortages, driving heightened interest in robotic harvesting technologies. Although multi-arm coordination in harvesting robots is considered a highly promising solution to this issue, it introduces technical challenges in achieving effective coordination. These challenges include mutual interference among multi-arm mechanical structures, task allocation across multiple arms, and dynamic operating conditions. This imposes higher demands on task coordination for multi-arm harvesting robots, requiring collision-free collaboration, optimization of task sequences, and dynamic re-planning. In this work, we propose a framework that models the task planning problem of multi-arm operation as a Markov game. First, considering multi-arm cooperative movement and picking sequence optimization, we employ a two-agent Markov game framework to model the multi-arm harvesting robot task planning problem. Second, we introduce a self-attention mechanism and a centralized training and execution strategy in the design and training of our deep reinforcement learning (DRL) model, thereby enhancing the model’s adaptability in dynamic and uncertain environments and improving decision accuracy. Finally, we conduct extensive numerical simulations in static environments; when the harvesting targets are set to 25 and 50, the execution time is reduced by 10.7% and 3.1%, respectively, compared to traditional methods. Additionally, in dynamic environments, both operational efficiency and robustness are superior to traditional approaches. The results underscore the potential of our approach to revolutionize multi-arm harvesting robotics by providing a more adaptive and efficient task planning solution. We will research improving the positioning accuracy of fruits in the future, which will make it possible to apply this framework to real robots. Full article

In this research, fully biobased composites consisting of poly(butylene 2,5-furandicarboxylate) (PBF) and cellulose nanocrystals (CNC) were successfully prepared through a common solution and casting method. The influence of CNC on the crystallization behavior, mechanical property, and hydrophilicity of PBF was systematically investigated. Under different crystallization processes, the crystallization of PBF was obviously promoted by CNC as a biobased nucleating agent. The Ozawa equation was not suitable to fit the nonisothermal melt crystallization kinetics of PBF and PBF/CNC composites. The nucleation activity of CNC was quantitatively calculated by the Dobreva method; moreover, the nucleation efficiency of CNC was further evaluated through the self-nucleation procedure. The isothermal melt crystallization kinetics of PBF and PBF/CNC composites was well described by the Avrami method; moreover, the crystallization mechanism and the crystal structure of PBF remained unchanged despite the presence of CNC. CNC also greatly enhanced both the mechanical property and hydrophilicity of PBF in the composites. In sum, low loadings of CNC simultaneously improved the crystallization, mechanical property, and hydrophilicity of PBF, which should be of significant importance and interest in fully biobased polymer composites from a sustainable viewpoint. Full article

Nanoparticle-based drug delivery systems hold great promise for improving the effectiveness of anti-tumor therapies. However, their clinical translation remains hindered by several significant challenges, including intricate preparation processes, limited drug loading capacity, and concerns regarding potential toxicity. In this context, pure drug-assembled nanosystems (PDANSs) have emerged as a promising alternative, attracting extensive research interest due to their simple preparation methods, high drug loading efficiency, and suitability for large-scale industrial production. This innovative approach presents new opportunities to enhance both the safety and therapeutic efficacy of cancer treatments. This review comprehensively explores recent progress in the application of PDANSs for cancer therapy. It begins by detailing the self-assembly mechanisms and fundamental principles underlying PDANS formation. The discussion then advances to strategies for assembling single pure drug nanoparticles, as well as the co-assembly of multiple drugs. Subsequently, the review addresses the therapeutic potential of PDANSs in combination treatment modalities, encompassing diagnostic and therapeutic applications. These include combinations of chemotherapeutic agents, phototherapeutic approaches, the integration of chemotherapy with phototherapy, and the synergistic use of immunotherapy with other treatment methods. Finally, the review highlights the potential of PDANSs in advancing tumor therapy and their prospects for clinical application, providing key insights for future research aimed at optimizing this technology and broadening its utility in cancer treatment. Full article

A method for cooperative transportation, which required formation change in a traveling environment, is gaining interest. Deep reinforcement learning is used in formation changes for multi-robot cases. The MADDPG (Multi-Agent Deep Deterministic Policy Gradient) method is popularly used for recognized environments. On the other hand, re-learning may be required in unrecognized circumstances by using the MADDPG method. Although the development of MADDPG using model-based learning and imitation learning has been applied to reduce learning time, it is unclear how the learning results are transferred when the number of robots changes. For example, in the GASIL-MADDPG (Generative adversarial self-imitation learning and Multi-agent Deep Deterministic Policy Gradient) method, how the results of three robot training can be transferred to the four robots’ neural networks is uncertain. Nowadays, Scaled Dot Product Attention (SDPA) has attracted attention and is highly impactful for its speed and accuracy in natural language processing. When transfer learning is combined with fast computation, the efficiency of edge-level re-learning is improved. This paper proposes a formation change algorithm that allows easy and fast multi-robot knowledge transfer using SDPA combined with MAPPO (Multi-Agent Proximal Policy Optimization), compared to other methods. This algorithm applies SDPA to multi-robot formation learning and performs fast learning by transferring the acquired knowledge of formation changes to a certain number of robots. The proposed algorithm is verified by simulating the robot formation change and was able to achieve dramatic high-speed learning capabilities. The proposed SDPA-MAPPO (Scaled Dot Product Attention-Multi-Agent Proximal Policy Optimization) learned 20.83 times faster than the Deep Dyna-Q method. Furthermore, using transfer learning from a three-robot to five-robot case, the method shows that the learning time can be reduced by about 56.57 percent. A scenario of three-robot to five-robot is chosen based on the number of robots often used in cooperative robots. Full article

The preparation of core–sheath fibers by electrospinning is a topic of significant interest for producing composite fibers with distinct core and sheath functionalities. Moreover, in core–sheath fibers, low-molecular-weight substances or nanosized inorganic additives can be deposited in a targeted manner within the core or the sheath. Commonly, for obtaining a core–sheath structure, coaxial electrospinning is used. It requires a coaxial spinneret and suitable immiscible solvents for the inner and outer solutions. The single-nozzle spinneret electrospinning of emulsions can address these issues, but use of a stabilizing agent is needed. A third approach—preparation of core–sheath fibers by single-nozzle spinneret electrospinning of homogeneous blend solutions of two polymers or of a polymer/low-molecular-weight substance—has been much less studied. It circumvents the difficulties associated with the coaxial and the emulsion electrospinning and is thoroughly discussed in this review. The formation of core–sheath fibers in this case is attributed to phase-separation-driven self-organization during the electrospinning process. Some possibilities for obtaining core–double sheath fibers using the same method are also indicated. The gained knowledge on potential applications of core–sheath fibers prepared by single-nozzle spinneret electrospinning of emulsions and homogeneous blend solutions is also discussed. Full article

Vascular wilt is an important tomato disease that affects culture yields worldwide, with Fusarium oxysporum (F.o) being the causal agent of this infection. Several management strategies have lost effectiveness due to the ability of this pathogen to persist in soil and its progress in vascular tissues. However, nowadays, research has focused on understanding the plant defense mechanisms to cope with plant diseases. One recent and promising approach is the use of extracellular DNA (eDNA) based on the ability of plants to detect their self-eDNA as damage-associated molecular patterns (DAMPs) and pathogens’ (non-self) eDNA as pathogen-associated molecular patterns (PAMPs). The aim of this work was to evaluate the effect of the eDNA of F.o (as a DAMP for the fungus and a PAMP for tomato plants) applied on soil, and of tomato’s eDNA (as a DAMP of tomato plants) sprayed onto tomato plants, to cope with the disease. Our results suggested that applications of the eDNA of F.o (500 ng/µL) as a DAMP for this pathogen in soil offered an alternative for the management of the disease, displaying significantly lower disease severity levels in tomato, increasing the content of some phenylpropanoids, and positively regulating the expression of some defense genes. Thus, the eDNA of F.o applied in soil was shown to be an interesting strategy to be further evaluated as a new element within the integrated management of vascular wilt in tomato. Full article

A system of incentives can be established to encourage several parties to unite as a community of interest and become jointly committed to the platform economic governance. The platform economy involves progressively more complex subjects of interest and relationships, which are not the typical principal–agent one-time cooperative relationship. This study investigates the problem of regulatory incentives in the platform economy, specifically focusing on the relationship between the government, platform enterprises, and merchants. It analyzes this issue under conditions of asymmetric information by constructing and solving a dual principal–agent model. The findings indicate the following: (1) the government’s incentives and regulatory mechanisms can be considered as interchangeable to some extent, with decisions made by evaluating their respective costs; (2) the government’s optimal incentives and regulations ultimately shape the self-regulatory behavior of merchants through platform enterprises; and (3) the optimal level of incentives for both the government and the platform enterprise is influenced by factors such as the ability coefficient, the social transformation coefficient, and the merchants’ reliance on the platform enterprise. Additionally, the optimal effort level of the platform enterprise and the merchants increases with higher levels of the regulatory effort, risk sensitivity coefficient, and ability coefficient. A win–win scenario and a long-term, stable cooperative partnership can be reached by the three parties under the ideal incentive intensity. The study’s conclusions can serve as a theoretical foundation and support for the creation of incentive contracts for platform economy regulation. Full article