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Microscale bioturbation as a first order control on distal prodelta to offshore mudrock reservoirs
Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions
RUL-DBRS: A Novel Energy Efficient and Robust Protocol for Enhanced Communication in Underwater Wireless Sensor Networks
A blockchain-enabled trust-aware authentication framework for secure communication in VANETs
Abstract Vehicular Ad Hoc Networks (VANETs) are essential for intelligent transportation systems; however, their dynamic topology and open wireless communication environment expose them to impersonation, Sybil, replay, and message modification attacks. Existing authentication schemes mainly rely on centralized authorities and conventional cryptographic mechanisms, which lack dynamic trust evaluation and fail to ensure secure authentication during Roadside Unit (RSU) handover and cluster mobility. To address these challenges, this paper proposes a blockchain-enabled trust-aware authentication framework for secure VANET communication. The framework integrates a Joint Probability and Error Unit-based Deep Learning Neural Network (JPEU-DLNN) for dynamic trust assessment and Log-based Edwards Curve Cryptography (LECC) for lightweight key generation. In addition, Gini Indexed Farthest First Clustering (GIFFC) ensures stable cluster formation, while the Directional Gannet Optimization Algorithm (DGOA) supports optimal routing. Blockchain technology provides decentralized and immutable credential storage, eliminating single-point failure and certificate forgery. Simulation results show that the proposed framework achieves 95.62% authentication accuracy and 96.94% packet delivery ratio, with reduced end-to-end delay and improved network lifetime compared to existing blockchain-based authentication schemes. Security evaluation confirms strong resistance against active and passive attacks. These results demonstrate the practical applicability of the framework for secure and reliable VANET deployments.
Efficiency of a microplastic removal system from synthetic wastewater using a chemical process combined with simple filtration
Abstract Microplastic contamination in freshwater ecosystems poses a critical environmental challenge, necessitating effective removal technologies in wastewater treatment. This research aims to evaluate the efficiency of a hybrid removal system for polyethylene microplastics (particle sizes 0.15–1 mm) by integrating chemical coagulation with simple sand filtration. The performance of three coagulants—alum, polyaluminium chloride (PAC), and ferric chloride—supplemented with chitosan as a coagulant aid, was optimized across various dosages and pH levels, followed by the evaluation of a sand filter column under different flow rates. The results indicated that coagulation efficacy was pH-dependent, peaking at pH 7, where PAC achieved the highest removal efficiency of 77.41 ± 4.47% for fine 0.15 mm particles, followed by alum (73.22 ± 3.54%) and ferric chloride (63.18 ± 3.63%); notably, the combined system operating at a flow rate of 15 mL/min (Experimental Set 1) attained an exceptional overall removal efficiency of 99.93%. These findings demonstrate that coupling chemical coagulation with low-cost sand filtration offers a robust and scalable solution for significantly reducing microplastic discharge in synthetic wastewater applications.
Design, modification, and manufacture of animal-drawn secondary tillage implements for wheat-based light soil farming systems in Misrak Bedawacho District, Central Ethiopia
Self-assembly of polyvinylidene fluoride (PVDF) and carbon nanotubes into flexible piezoelectric nanofiber transducers
Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology
Abstract Anemia’s high global prevalence and socio-economic burden necessitate early diagnosis, yet reliance on invasive blood testing creates significant barriers to diagnosis and treatment. To address this, we developed a deep learning model using the Detection Transformer framework for the rapid, non-invasive assessment of anemia severity in a real-world emergency department setting. Comparing a lip-focused model to a full-face approach, the former proved superior, achieving 85.0% accuracy. This significantly outperformed the full-face model (77.0%) and clinical judgments by both senior (59.3%) and junior (49.95%) physicians, with a rapid processing time of 127.50 ms. By integrating key medical knowledge to classify anemia into three severity levels, our model surpasses clinician performance, demonstrating its potential as a powerful, automated tool for clinical decision support.
Pyrite-assisted mechanochemical leaching of chalcopyrite concentrate in sulfuric acid under non-oxidative conditions
Thymoquinone attenuates tricyclazole-induced testicular dysfunction through endocrine, redox, histomorphometric and sperm function changes in rats
Development and multi-institutional validation of a deep learning algorithm for predicting cervical cord compression using dynamic cervical lateral radiographs
Relationship between sleep, stress and psychological distress in internally displaced persons depends on the length of displacement
Abstract In recent years, the number of refugees and internally displaced persons (IDPs) has dramatically grown worldwide. We aimed to examine psychological distress and its association with sleep and stress in two groups of IDPs with different lengths of displacement in comparison with the general population. Forty-five individuals displaced in 2008, 2.5 months before this study (2008 IDPs), 67 respondents displaced in 1993 (1993 IDPs), and 53 individuals from Tbilisi general population were assessed. All study participants completed the Insomnia Severity Index (ISI), Perceived Stress Scale (PSS) and Brief Symptom Inventory (BSI). Sociodemographic information such as age, sex, education level and marital status, was collected. The groups differed significantly on age, education, marital status, ISI, PSS, and Global Severity Index (GSI). The highest mean ISI and PSS scores were found for the 2008 IDPs, while the highest GSI score was observed in 1993 IDPs, although the GSI and PSS scores did not differ between the two groups of IDPs. The proportion of people with a GSI T score ≥ 63 was 47.2% in the general population, 71.6% in the 1993 IDPs, and 64.4% in the 2008 IDPs. The ISI and PSS were significant predictors of the GSI in all groups. The PSS exhibited the highest predictive power for 2008 IDPs (β = 0.429), while the ISI - in 1993 IDPs (β = 0.417). Psychological distress persists over time in IDPs. Although both stress and insomnia are strongly associated with psychological distress, perceived stress appears to have a stronger impact in the early resettlement period, whereas insomnia emerges as a more prominent predictor later, years after displacement. Therefore, sleep health in displaced population, and on a broader scale in individuals subjected to traumatic events, warrant attention in long-term perspective for implementing targeted interventions and effectively addressing well-being of affected individuals.
Prognostic value of on-admission IL-6, lactate, base deficit, ESR, and CRP in a prospective study of multiple trauma patients
A multi-model carbon estimation framework for new urban district planning-integrating land use, transportation, and investment-based emission models
Temperature‐ and Light‐Regulated Liquid Crystal Smart Window for Dynamic Control of Daylight and Solar Heat in All‐Weather Conditions
ABSTRACT Smart windows that function in response to external conditions provide a promising approach to reduce heating, ventilation, and air conditioning energy consumption. However, it remains a major challenge to develop a smart window with environmental adaptability, multiple working states, and most importantly, the ability of dynamic management of solar light and heat with a simple and versatile molecular‐designed material. Here, we present a temperature‐ and light‐regulated smart window based on the interplay of light‐driven molecular motors and liquid crystal (LC) polymers. The window can dynamically switch among three distinct working states: transparent, reflective, and scattering, depending on ambient temperature and solar light intensity. The fast switching of working states enables excellent modulation of visible light transmittance (Δ T lum = 75.2%) and near‐infrared light transmittance (Δ T NIR = 49.3%), showing effective management of daylight and solar heat gain indoors. Simulation of energy regulation demonstrates that the smart window significantly reduces energy demand for indoor cooling and, therefore, is suitable for cities with different climate conditions. Our system provides an attractive approach toward more effective smart windows for sustainable and energy‐efficient green buildings.
Ion exchange resin-catalyzed epoxidation of sesame oil: effects of temperature and reaction time
Multimodal Structure Solution Unravels Correlated Disorder Promoting Ionic Migration in Silicate Sodium‐Ion Electrolytes
ABSTRACT Characterizing structural disorder in solids is of significant challenge, which requires new strategy on probing and describing local structures over different scales and uncovering coherent ordering hidden in the structural disorder. Herein, we demonstrate a multimodal solution for structural disorder in Na 2(1− x ) Mg 1− x Si 1+ x O 4 sodium‐ion electrolytes with a stuffed cristobalite tetrahedral network. Neutron pair distribution function analysis combined with reverse Monte Carlo simulations was employed to probe the structural disorder in nanometer‐scale supercells, uncovering the hidden correlated Mg/Si disorder or local Mg/Si order forming neighboring pure Mg (or Mg‐rich) and Si columns. This correlated disorder was further validated by solid state 29 Si nuclear magnetic resonance (NMR) spectroscopy and NMR‐guided structure screenings. The sodium cations in the tunnels were proposed to be interstitial‐like mobile charge carriers for ionic conduction in Na 2(1− x ) Mg 1− x Si 1+ x O 4 referring to the parent cristobalite structure. Both sodium contents within the tunnels and structural disorder level play competing roles in the sodium migration, while local Mg/Si order may minimize distortion of tetrahedral network and therefore maximize the tunnel bottlenecks promoting sodium migration. This work provides practicable multimodal solution strategy to solve the commonly complex structural disorders and unveil inherently local order with wide applicability in functional materials, enhancing understanding of structure‐property relationship.
The effect of 6-day fasting on physical performance and neuromuscular control in healthy men
Core‐to‐Wing Type Hybrid Dimeric Giant Molecule Acceptors With Different‐Length Ester‐Linked Alkyl Chains Enable 20.25% Efficiency Organic Solar Cells
ABSTRACT Dimeric acceptors have recently emerged as promising giant molecule acceptors (GMAs) for organic solar cells (OSCs), but most systems link two identical monomeric units. Hybrid GMAs combining different acceptor units remain unexplored. Herein, we develop a core‐to‐wing type hybrid strategy coupling BT‐ and BZ‐core acceptor units through flexible ester‐linked alkyl chains, affording three GMAs (BTZ‐2‐2, BTZ‐2‐6, and BTZ‐2‐10). This design enables complementary absorption by combining the spectral features of BT‐ and BZ‐based monomers while preserving favorable crystallization behavior. Among them, BTZ‐2‐6 exhibits the broadest, most red‐shifted absorption, enhanced π‐π stacking, and the highest electron mobility due to the optimal length of its ester‐linked alkyl chain. The PM6:BTZ‐2‐6 binary device delivers 18.53% efficiency and retains nearly 90% of its initial efficiency after 720 h of illumination. Furthermore, the efficiency can be increased to 19.41% by replacing PM6 with D18 as the donor polymer, and further boosted to 20.25% by incorporating BTZ‐2‐6 as a third component into PM6:L8‐BO binary devices. These results demonstrate that core‐to‐wing hybrid GMAs with ester‐linked alkyl chains of different lengths provide an effective strategy for constructing high‐performance GMAs, offering new opportunities for efficient and stable OSCs.