Search Results (3,655)

Plasma-activated water (PAW), enriched with reactive oxygen and nitrogen species (RONS), presents oxidative and antimicrobial characteristics with potential in cosmetic applications. This study examined the effects of two PAW formulations—nitrate-rich (PAW-N) and peroxide-rich (PAW-P)—on human hair types classified as straight (Type 1), wavy (Type 2), and coily/kinky (Type 4). The impact of PAW on hair structure and chemistry was evaluated using Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), UV–Vis spectrophotometry, and physicochemical analyses of the liquids (pH, ORP, conductivity, and TDS). PAW-N, with high nitrate content (~500 mg/L), low pH (2.15), and elevated conductivity (6244 µS/cm), induced significant damage to porous hair types, including disulfide bond cleavage, protein oxidation, and lipid degradation, as indicated by FTIR and EDS data. SEM confirmed severe cuticle disruption. In contrast, PAW-P, containing >25 mg/L of hydrogen peroxide and exhibiting milder acidity and lower ionic strength, caused more localized and controlled oxidation with minimal morphological damage. Straight hair showed greater resistance to both treatments, while coily and wavy hair were more susceptible, particularly to PAW-N. These findings suggest that the formulation and ionic profile of PAW should be matched to hair porosity for safe oxidative treatments, supporting the use of PAW-P as a gentler alternative in hair care technologies. Full article

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning. Full article

Background/Objectives: Autism spectrum disorder (ASD) has been extensively studied through neuroimaging, primarily focusing on grey matter and more in children than in adults. Studies in children and adolescents fail to capture changes that may dampen with age, thus leaving only changes specific to ASD. While grey matter has been the primary focus, white matter (WM) may be more specific in identifying the particular biological signature of the neurodiversity of ASD. Diffusion tensor imaging (DTI) is the more appropriate tool to investigate WM in ASD. Despite being introduced in 1994, its application to ASD research began in 2001. Studies employing DTI identify altered fractional anisotropy (FA), mean diffusivity, and radial diffusivity (RD) in individuals with ASD compared to typically developing (TD) individuals. Methods: We systematically reviewed literature on 21 May 2025 on PubMed using the following strategy: (“autism spectrum”[ti] OR autistic[ti] OR ASD[ti] OR “high-functioning autism” OR Asperger*[ti] OR Rett*[ti]) AND (DTI[ti] OR “diffusion tensor”[ti] OR multimodal[ti] OR “white matter”[ti] OR tractograph*[ti]). Our search yielded 239 results, of which 26 were adult human studies and eligible. Results: Analysing the evidence, we obtained regionally diverse WM alterations in adult ASD, specifically in FA, MD, RD, axial diffusivity and kurtosis, neurite density, and orientation dispersion index, compared to TD individuals, mostly in frontal and interhemispheric tracts, association fibres, and subcortical projection pathways. These alterations were less prominent than those of children and adolescents, indicating that individuals with ASD may improve during brain maturation. Conclusions: Our findings suggest that white matter alterations in adults with ASD are regionally diverse but generally less pronounced than in younger populations. This may indicate a potential improvement or adaptation of brain structure during maturation. Further research is needed to clarify the neurobiological mechanisms underlying these changes and their implications for clinical outcomes. Full article

Groundwater is one of the main water sources for consumption, domestic use, agriculture, and tourism in coastal communities. However, high total dissolved solids (TDS) levels in the water (700–2000 mg L⁻¹ TDS) and electrical conductivity (3000–5000 µS cm⁻¹) threaten the health and economic growth opportunities for residents. This research aims to evaluate the performance of a laboratory-scale electrodialysis system as a technology for desalinating brackish water. For this purpose, water samples were collected from real groundwater sources. Batch experiments were conducted with varying operational parameters, such as voltage (2–10 V), feed volume (100–1600 mL), recovery rate (50–80%), and cros-flow velocity (1.3–5.1 cm s⁻¹) to determine the electrodialysis system setup that meets the requirements for drinking water in terms of TDS and energy efficiency. A total specific energy consumption of 1.65 kWh m⁻³, including pumping energy, was achieved at a laboratory scale. The conditions were as follows: flow velocity of 5.14 cm s⁻¹, applied voltage of 6 V, feed volume of 1.6 L, and a water recovery of 66%. Furthermore, increasing the flow velocity and the applied voltage enhanced the desalination kinetics and salt removal. Additionally, the system presented opportunities for scalability. This research aims to evaluate a sustainable membrane-based treatment technology for meeting the growing demand for water resources in coastal communities, particularly in developing countries in South America. Full article

This study aimed to evaluate the effect of replacing chalk with fly ash in a two-component polyurethane (2C PU) adhesive on its physicochemical, mechanical, and environmental properties, as a practical application of circular economy principles. Six adhesive formulations were prepared, each containing a chalk-to-fly ash ratio as a filler. The study evaluated rheological, mechanical, thermal, and environmental parameters. Mechanical tests confirmed cohesive failure within the bonded material, indicating that the bond strength at the adhesive–substrate interface exceeded the internal strength of the substrate. The highest contaminant elution levels recorded were 0.62 mg/kg for molybdenum and 0.20 mg/kg for selenium, which represent only 6.2% and 40% of the regulatory limits, respectively. Dissolved organic carbon (DOC) and total dissolved solids (TDS) did not exceed 340 mg/kg and 4260 mg/kg, respectively. GC-MS analysis did not reveal the presence of prominent volatile organic compound emissions. Initial screening suggests possible compatibility with low-emission certification schemes (e.g., A+, AgBB, EMICODE^®), though confirmation requires further quantitative testing. The results demonstrate that fly ash can be an effective substitute for chalk in polyurethane adhesives, ensuring environmental compliance and maintaining functional performance while supporting the principles of the circular economy. Full article

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. Full article

Background/Objectives: Real-world evidence shows alarmingly suboptimal utilization of guideline directed medical therapy (GDMT) in heart failure with reduced ejection fraction (HFrEF). One of the barriers of GDMT implementation appears to be concerns about the potential development of drug-related adverse events (AEs), particularly in high-risk patients. This study aimed to evaluate whether advanced HFrEF (AHF) patients can be up-titrated safely and whether AHF predisposes individuals to the occurrence of putatively drug-related AEs. Methods: A total of 373 HFrEF patients with documented baseline, 2 months, and 12 months visits were analyzed for utilization and target dosages (TDs) of HF drugs. Successful up-titration and AEs were evaluated for different stages of HF reflected by N-terminal pro-B type natriuretic peptide (NT-proBNP) (<1000 pg/mL, 1000–2000 pg/mL, >2000 pg/mL). Results: A stepwise increase in HF medications was observed for all drug classes during follow-up. At 12 months, 73%, 75%, 62%, 86%, and 45% of patients received ≥90% of TDs of beta-blockers (BBs), renin–angiotensin system inhibitors (RASis), mineralocorticoid receptor antagonists (MRAs), sodium–glucose cotransporter-2 inhibitors (SGLT2 i), and triple-therapy, respectively. Predictors of successful up-titration in logistic regression were baseline HF drug TDs, estimated glomerular filtration rate (eGFR), and potassium, but not NT-proBNP or age. The development of AEs was rare, with hyperkalemia as the most common event (34% at 12 months). AEs were comparable in all stages of HF. However, the development of hyperkalemia was more frequent in patients with higher NT-proBNP and also accounted for most cases of incomplete up-titration. Conclusions: This study suggests that with dedicated protocols and frequent visits, GDMT can be successfully implemented across all stages of HFrEF, including patients with AHF. Full article

Articulated Tugs and Barges (ATBs) are increasingly recognised for their effectiveness in transporting chemicals, petroleum, bulk goods, and containers, primarily due to their exceptional flexibility and fuel efficiency. Recent projections indicate that the ATB market is on track for significant growth, which is expected to lead to an increase in the annual growth rate from 2025 to 2032. This study aims to analyse the current advancements in ATB technology and provide insights into the ATB fleet and the systems that connect tugboats and barges. Furthermore, it highlights the advantages of this transportation system, especially regarding its role in enhancing energy efficiency within the maritime transport sector. Currently, there is limited information available in the public domain about ATBs compared to other commercial vessels. The analysis reveals that much of the required information for modern ATB design is not accessible outside specialised design companies. The study also focuses on conceptual design aspects, which include the main dimensions, articulated connections, propulsion systems, and machinery, concluding with an evaluation of economic viability. Special emphasis is placed on defining the main dimensions, which is a critical part of the complex design process. In this context, the ratios of length to beam (L/B), beam to draft (B/D), beam to depth (B/T), draft to depth (T/D), and power to the number of tugs cubed (Pw/N³) are established as design control parameters in the conceptual design phase. This aspect underscores the novelty of the present study. Additionally, the economic viability is analysed in terms of both CAPEX (capital expenditures) and OPEX (operational expenditures). While CAPEX does not significantly differ between the methods used in different types of commercial ships, OPEX should account for the unique characteristics of ATB vessels. Full article

The aim of this study was to analyze and compare the external load demands of players from the Spanish women’s national football teams across the U-17, U-20, and senior categories during their respective FIFA World Cup participations. Key kinematic variables were assessed via global positioning systems (GPS), including total distance (TD), high-speed running (HSR; ≥18 km·h⁻¹), sprint distance (≥21 km·h⁻¹), accelerations (>3 m·s⁻²), decelerations (<–3 m·s⁻²), and high metabolic load distance (HMLD) during 3 world cups (U17, U20 and senior). Significant differences were observed between the senior team and both U-20 and U-17 in nearly all variables, with greater magnitude as the intensity of the metrics increased, showing effect sizes ranging from moderate to very large (d = 0.95 to 4.76). Positional analysis by categories showed that senior full backs (FB) and central midfielders (CM) showed higher demands compared to U-20 and U-17. For TD, senior covered more than U-17 (FB: p = 0.001; d = 1.11 | CM: p = 0.023; d = 0.97), with small differences vs. U-20 (d ≤ 0.54). In HSR, both positions outperformed U-17 and U-20 (FB: p ≤ 0.007; d = 0.87–1.15 | CM: p ≤ 0.031; d = 0.71–1.11). In HMLD, both FB and CM displayed very large differences compared to U-17 and U-20 (all p < 0.001; d = 2.54–6.16). These findings underscore the need for progressive development of locomotor capacities from early stages, considering both age category and playing position, to facilitate a more seamless transition to elite-level football. Full article

With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions. Full article

A new bicyclic polyketide, amesilide (1), along with the previously reported metabolites, chamisides A (2), B (3), and E (4), chaetoconvosins B (5) and C (6), and chaetochromins A (7) and B (8), were isolated from the marine fungus Amesia nigricolor MUT6601. The structures of the compounds were determined by extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses, as well as specific rotation. Absolute configurations of the stereogenic centers of amesilide (1) were determined by a comparison of its experimental circular dichroism (CD) spectrum with its time-dependent density functional theory (TD-DFT) electronic circular dichroism (ECD) spectra. Among them, chaetochromins A (7) and B (8) showed strong antibacterial activity against Staphylococcus aureus S25 (MBC values of 12.50 µM and MIC values of 6.25 µM) and a moderate cytotoxicity against monocytes (THP-1) and peripheral blood cells (PBMC) (IC₅₀ values of 33.65–40.01 µM). Full article

Magnetic nanoparticles (MNPs), especially iron oxide (Fe₃O₄), display distinctive superparamagnetic characteristics and elevated surface-area-to-volume ratios, facilitating improved physicochemical interactions with solutes and pollutants. These characteristics make MNPs strong contenders for use in water treatment applications. This research investigates the application of iron oxide MNPs synthesized via co-precipitation as innovative draw solutes in forward osmosis (FO) for treating synthetic produced water (SPW). The FO membrane underwent surface modification with sulfobetaine methacrylate (SBMA), a zwitterionic polymer, to increase hydrophilicity, minimize fouling, and elevate water flux. The SBMA functional groups aid in electrostatic repulsion of organic and inorganic contaminants, simultaneously encouraging robust hydration layers that improve water permeability. This adjustment is vital for sustaining consistent flux performance while functioning with MNP-based draw solutions. Material analysis through thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR) verified the MNPs’ thermal stability, consistent morphology, and modified surface chemistry. The FO experiments showed a distinct relationship between MNP concentration and osmotic efficiency. At an MNP dosage of 10 g/L, the peak real-time flux was observed at around 3.5–4.0 L/m²·h. After magnetic regeneration, 7.8 g of retrieved MNPs generated a steady flow of ~2.8 L/m²·h, whereas a subsequent regeneration (4.06 g) resulted in ~1.5 L/m²·h, demonstrating partial preservation of osmotic driving capability. Post-FO draw solutions, after filtration, exhibited total dissolved solids (TDS) measurements that varied from 2.5 mg/L (0 g/L MNP) to 227.1 mg/L (10 g/L MNP), further validating the effective dispersion and solute contribution of MNPs. The TDS of regenerated MNP solutions stayed similar to that of their fresh versions, indicating minimal loss of solute activity during the recycling process. The combined synergistic application of SBMA-modified FO membranes and regenerable MNP draw solutes showcases an effective and sustainable method for treating produced water, providing excellent water recovery, consistent operational stability, and opportunities for cyclic reuse. Full article