Search Results (89,226)

Research highlights a growing demand for active, experiential learning methods in higher education, especially in teacher education. While the benefits of parental involvement (PI) are well-documented, Hungary lacks tools to effectively prepare teacher trainees for fostering family–school cooperation. This study addresses this gap by introducing a custom-designed board game as an innovative teaching tool. The game simulates real-world challenges in PI through a cooperative, scenario-based framework. Exercises are grounded in international and national research, ensuring their relevance and evidence-based design. Tested with 110 students, the game’s educational value was assessed via post-gameplay questionnaires. Participants emphasized the strengths of its cooperative structure, realistic scenarios, and integration of humor. Many reported gaining new insights into parental roles and strategies for effective home–school partnerships. Practical applications include integrating the game into teacher education curricula and adapting it for other educational contexts. This study demonstrates how board games can bridge theory and practice, offering an engaging, effective medium to prepare future teachers for the challenges of PI. Full article

Computational Thinking (CT) has been an important concept for the computer science education community in the last 20 years. In this work we performed a systematic review of the literature regarding the computational thinking of children from kindergarten to primary school. We compiled a large dataset of one hundred and twenty (120) studies from the literature. Through analysis of these studies, we tried to reveal important insights and draw interesting and valid conclusions. We analyzed various qualitative and quantitative aspects of the studies, including the sample size, the year of publication, the country of origin, the studies’ design and duration, the computational tools used, and so on. An important aspect of the work is to highlight differences between different study designs. We identified a total of 120 studies, with more than half of them (>50%) originating from Asian countries. Most studies (82.5%) conducted some form of intervention, aiming to improve their computational thinking in students. A smaller proportion (17.5%) were assessment studies in which the authors conducted assessments regarding the children’s computational thinking. On average, intervention studies had a smaller number of participants, but differences in duration could not be identified. There was also a lack of large-scale longitudinal studies. Block-based coding (i.e., Scratch) and Plugged and Unplugged activities were observed in high numbers in both categories of studies. CT assessment tools showed great variability. Efforts for standardization and reaching a consensus are needed in this regard. Finally, robotic systems have been found to play a major role in interventions over the last years. Full article

One of the promising research areas involving carborane derivatives is boron neutron capture therapy (BNCT). Due to the high boron atom content in carborane molecules, these compounds are considered potential candidates for BNCT-based cancer treatment. Despite ongoing studies on various biologically active carboranyl-containing compounds, the search continues for substances that meet the stringent requirements of effective BNCT agents. In this study, the synthesis of carboranyl-containing derivatives of β-arylaliphatic acids is described, along with the investigation of their reactivity with primary and secondary amines, as well as with metals and their hydroxides. The molecular structures of the synthesized compounds were confirmed using Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, elemental analysis, and mass spectrometry (LC-MS). Cytotoxicity of the water-soluble compound potassium 3-(2-isopropyl-1,2-dicarba-closo-dodecaboran-1-yl)-3-phenylpropanoate was evaluated using several cell lines, including HdFn and MCF-7. Full article

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

The potent and selective ‘genetic zipper’ method for insect pest control consists of three essential components: an antisense DNA (the finder), its complementary mature rRNA or pre-rRNA of the pest (the target), and the host’s endogenous DNA-guided rRNase (the degrader). Although this approach has been validated, the spectrum of effective rRNA targets remains insufficiently explored. In this study, we report for the first time the insecticidal efficacy of a novel oligonucleotide insecticide, Eriola-11, which targets the mitochondrial 16S rRNA of the woolly apple aphid Eriosoma lanigerum Hausmann. We hypothesized that the antisense-mediated silencing of mitochondrial rRNA would impair aphid viability and lead to physiological disruptions associated with mitochondrial energy metabolism. Eriola-11 was applied either once or twice (with a 24 h interval) to aphid-infested plants, and aphid mortality was recorded over 14 days. Mitochondrial 16S rRNA expression levels were quantified using molecular assays, and the degradation kinetics of Eriola-11 were assessed in aphid tissue homogenates. Results showed significant insecticidal activity, with 67.55% mortality after a single treatment and 83.35% after two treatments. Treated aphids exhibited the loss of their characteristic white woolly wax covering, and mitochondrial 16S rRNA expression was reduced 0.66-fold relative to the control. Additionally, Eriola-11 was fully degraded by aphid DNases from tissue homogenates within 3 h, highlighting its rapid biodegradability. These findings establish mitochondrial 16S rRNA as a viable target for antisense insecticides and expand the catalogue of potential rRNA-based targets, offering a promising avenue for environmentally sustainable pest control strategies. Full article

Collagen type I (COL(I)) is a key component of the extracellular matrix (ECM) and is involved in cell signaling and migration through cell receptors. Collagen degradation produces bioactive peptides (matrikines), which influence cellular processes. In this study, we investigated the biological effects of nine most abundant, naturally occurring urinary COL(I)-derived peptides on human endothelial cells at physiological concentrations, using cell migration assays, mass spectrometry-based proteomics, flow cytometry, and AlphaFold 3. While none of the peptides significantly altered endothelial migration by themselves at physiological concentrations, full-length COL(I) increased cell migration, which was inhibited by Peptide 1 (²²⁹NGDDGEAGKPGRPGERGPpGp²⁴⁹). This peptide uniquely contains the DGEA and GRPGER motifs, interacting with integrin α2β1. Flow cytometry confirmed the presence of integrin α2β1 on human endothelial cells, and AlphaFold 3 modeling predicted an interaction between Peptide 1 and integrin α2. Mass spectrometry-based proteomics investigating signaling pathways revealed that COL(I) triggered phosphorylation events linked to integrin α2β1 activation and cell migration, which were absent in COL(I) plus peptide 1-treated cells. These findings identify Peptide 1 as a biologically active COL(I)-derived peptide at a physiological concentration capable of modulating collagen-induced cell migration, and provide a foundation for further investigation into its mechanisms of action and role in urine excretion. Full article

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

The response time of Artificial Neural Network (ANN) inference is critical in embedded systems processing sensor data close to the source. This is particularly important in applications such as predictive maintenance, which rely on timely state change predictions. This study enables estimation of model response times based on the underlying platform, highlighting the importance of benchmarking generic ANN applications on edge devices. We analyze the impact of network parameters, activation functions, and single- versus multi-threading on response times. Additionally, potential hardware-related influences, such as clock rate variances, are discussed. The results underline the complexity of task partitioning and scheduling strategies, stressing the need for precise parameter coordination to optimise performance across platforms. This study shows that cutting-edge frameworks do not necessarily perform the required operations automatically for all configurations, which may negatively impact performance. This paper further investigates the influence of network structure on model calibration, quantified using the Expected Calibration Error (ECE), and the limits of potential optimisation opportunities. It also examines the effects of model conversion to Tensorflow Lite (TFLite), highlighting the necessity of considering both performance and calibration when deploying models on embedded systems. Full article

Recently, a number of natural biologically active substances have been proven to be attractive alternatives to conventional anticancer medicine or as adjuvants in contemporary combination therapies. Although lignin-based materials were previously accepted as waste materials with limited usefulness, recent studies increasingly report the possibility of their use for novel applications in various industrial branches, including biomedicine. In this regard, the safety, efficiency, advantages and limitations of lignin compounds for in vitro/in vivo applications remain poorly studied and described. This study was carried out to investigate the possibility of using newly synthesized, alkali lignin-based micro-/nano-biopolymer formulations (Lignin@Formulations/L@F) as carriers for substances with antioxidant and/or anticancer effectiveness. Moreover, we tried to assess the opportunity for using an electro-assisted approach for achieving improved intracellular internalization. An investigation was conducted on an in vitro panel of breast cell lines, namely two breast cancer lines with different metastatic potentials and one non-tumorigenic line as a control. The characterization of all tested formulations was performed via DLS (dynamic light scattering) analysis. We developed an improved separation procedure via size/charge unification for all types of Lignin@Formulations. Moreover, in vitro applications were investigated. The results demonstrate that compared to healthy breast cells, both tested cancer lines exhibited slight sensitivity after treatment with different formulations (empty or loaded with antioxidant substances). This effect was also enhanced after applying electric pulses. L@F loaded with Quercetin was also explored only on the highly metastatic cancer cell line as a model for the breast cancer type most aggressive and non-responsive to traditional treatments. All obtained data suggest that the tested formulations have potential as carriers for the electro-assisted delivery of natural antioxidants such as Quercetin. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

Based on the principle of symmetry, we propose a variable step-size FxLMS algorithm with double nonlinear functions cooperative coupling (DNVSS-FxLMS), aiming to optimize the contradiction between convergence rate and steady-state error in the active pressure pulsation control system of hydraulic systems. The algorithm innovatively couples two types of nonlinear mechanisms (rational-fractional and exponential-function-based), constructing a refined error-step mapping relationship to achieve a balance between rapid convergence and low steady-state error. Simulation experiments were conducted considering the complex time-varying operating environment of a simulation-based hydraulic system. The results demonstrate that, when the system undergoes unstable random changes, the DNVSS-FxLMS algorithm converges at least twice as fast as traditional and existing variable step size algorithms, while reducing steady-state error by 2–5 dB. The proposed DNVSS-FxLMS algorithm exhibits significant advantages in convergence rate, steady-state error reduction, and tracking capability, providing a highly efficient and robust solution for real-time active control of hydraulic system pressure pulsation under complex operating conditions. Full article

This study reports the synthesis and evaluation of iron molybdate (Fe₂(MoO₄)₃) and iron tungstate (FeWO₄) as photocatalysts for the degradation of a real industrial effluent from aluminum anodizing processes under visible light irradiation. The oxides were synthesized via a co-precipitation method in an aqueous medium, followed by microwave-assisted hydrothermal treatment. Structural and morphological characterizations were performed using X-ray diffraction, field-emission scanning electron microscopy, Raman spectroscopy, ultraviolet–visible (UV–vis), and photoluminescence (PL) spectroscopies. The effluent was characterized by means of ionic chromatography, total organic carbon (TOC) analysis, physicochemical parameters (pH and conductivity), and UV–vis spectroscopy. Both materials exhibited well-crystallized structures with distinct morphologies: Fe₂(MoO₄)₃ presented well-defined exposed (001) and (110) surfaces, while FeWO₄ showed a highly porous, fluffy texture with irregularly shaped particles. In addition to morphology, both materials exhibited narrow bandgaps—2.11 eV for Fe₂(MoO₄)₃ and 2.03 eV for FeWO₄. PL analysis revealed deep defects in Fe₂(MoO₄)₃ and shallow defects in FeWO₄, which can influence the generation and lifetime of reactive oxygen species. These combined structural, electronic, and morphological features significantly affected their photocatalytic performance. TOC measurements revealed degradation efficiencies of 32.2% for Fe₂(MoO₄)₃ and 45.3% for FeWO₄ after 120 min of irradiation. The results highlight the critical role of morphology, optical properties, and defect structures in governing photocatalytic activity and reinforce the potential of these simple iron-based oxides for real wastewater treatment applications. Full article

Poly(organo)phosphazenes (PPZs) are increasingly recognized as versatile biomaterials for drug delivery applications in nanomedicine. Their unique hybrid structure—featuring an inorganic backbone and highly tunable organic side chains—confers exceptional biocompatibility and adaptability. Through precise synthetic methodologies, PPZs can be engineered to exhibit a wide spectrum of functional properties, including the formation of multifunctional nanostructures tailored for specific therapeutic needs. These attributes enable PPZs to address several critical challenges associated with conventional drug delivery systems, such as poor pharmacokinetics and pharmacodynamics. By modulating solubility profiles, enhancing drug stability, enabling targeted delivery, and supporting controlled release, PPZs offer a robust platform for improving therapeutic efficacy and patient outcomes. This review explores the fundamental chemistry, biopharmaceutical characteristics, and biomedical applications of PPZs, particularly emphasizing their role in zero-dimensional nanotherapeutic systems, including various nanoparticle formulations. PPZ-based nanotherapeutics are further examined based on their drug-loading mechanisms, which include electrostatic complexation in polyelectrolytic systems, self-assembly in amphiphilic constructs, and covalent conjugation with active pharmaceutical agents. Together, these strategies underscore the potential of PPZs as a next-generation material for advanced drug delivery platforms. Full article

This study investigated the effect of an apple pectin coating enriched with gamma-decalactone (GDL) on the physicochemical and microbiological quality of strawberries over 9 days of refrigerated storage. Strawberries were coated with pectin solutions containing a plasticizer and emulsifier, with or without GDL, and compared to uncoated controls. The coatings were evaluated for their effects on fruit mass loss, pH, extract content (°Brix), firmness, color parameters (L*, a*, b*, C*, h*, ΔE), and microbial spoilage. The pectin coating limited changes in extract, pH, and color and slowed firmness loss. Notably, GDL-enriched coatings significantly reduced spoilage (14.29% after 9 days vs. 57.14% in the control) despite accelerating pulp softening. Extract content increased the most in the GDL group (from 9.92 to 12.00 °Brix), while mass loss reached up to 22.8%. Principal Component Analysis (PCA) confirmed coating type as a major factor differentiating sample quality over time. These findings demonstrate the potential of bioactive pectin-based coatings to enhance fruit preservation and support the development of active packaging strategies. Further studies should optimize coating composition and control the release kinetics of functional compounds. Full article

Aizoon africanum (L.) Klak (Synonym Galenia africana L.) is traditionally used for a variety of medicinal purposes; however, it has been reported to cause liver damage and severe ascites, particularly in sheep and Angora goats in the arid regions of the Western Cape. This study explores its cytotoxic properties to identify potential cytotoxic compound(s) in the plant. The methanolic extract of A. africanum was re-investigated and subjected to various chromatographic techniques, including preparative HPLC, resulting in the isolation of eight compounds (1–8). Structural elucidation was primarily based on NMR data. Among the isolated compounds, four were flavanones, one was a flavonone, and three were chalcones. Notably, compound 8 was identified as a new chalcone, while compounds 2 and 3 were reported for the first time from this plant. The toxicity of these isolated compounds was evaluated against the HepG2 and SH-SY5Y cancer cell lines using the MTT assay. We further investigated markers of cell death using spectrophotometric and luminometric methods. Among the isolated compounds, 7 and 8 exhibited cytotoxic activities within the range of 3.0–20.0 µg/mL. Notably, the compounds demonstrated greater cytotoxicity towards liver-derived HepG2 cells compared to the neuronal SH-SY5Y cell line. Compound 7 (2′,4′-dihydroxychalcone) was identified as inducing apoptosis through the intrinsic pathway without causing overt necrosis. The findings indicate that the phytochemicals derived from A. africanum exhibit differential cytotoxic effects based on cell type, suggesting potential for developing novel anticancer agents, particularly compound 7. Additionally, the identification of compound 8 provides insight into the liver toxicity of this plant observed in sheep in South Africa. Full article