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Perceptual no-reference image quality assessment with meta-learning by graph representation learning and multi-scale feature fusion

No-reference image quality assessment (NR-IQA) aims to predict perceptual quality in alignment with the human visual system (HVS), yet existing methods face challenges in capturing long-range dependencies across distortion types and levels while preserving content fidelity during preprocessing. This paper presents a perceptually-driven NR-IQA framework that integrates meta-learning, graph representation learning, and multi-scale feature fusion to address these limitations. First, a meta-learning paradigm is employed to pre-train a self-calibrated convolutional backbone, which adaptively models spatial and channel-wise dependencies across scales, thereby enhancing the extraction of distortion-aware features while mitigating information loss caused by fixed-input preprocessing. Second, a graph representation learning module is introduced to explicitly encode the hierarchical relationships among distortion types, distortion levels, and image content. Nodes in the graph correspond to distorted images, while edges capture inter-sample similarities; these are jointly optimized via a graph convolutional network under dual supervision from a triplet-based distortion-type discriminator and a probabilistic distortion-level regressor that accounts for content-induced uncertainty. Extensive experiments on four benchmark datasets demonstrate that our method achieves better performance, with average SROCC and PLCC improvements of 3.6–36.6% over hand-crafted feature-based methods and consistent gains over deep learning-based approaches. Ablation studies and visualizations confirm that the proposed components collectively yield a more discriminative and generalizable distortion representation, closely mirroring human perceptual judgments.

Pulsed Charging‐Induced Interfacial Stabilization Enables Highly Stable Iron Phosphate Electrode for Ultrafast Electrochemical Lithium Extraction

ABSTRACT Amid the soaring global demand for lithium, electrochemical lithium extraction using iron phosphate (FePO 4 ) electrodes is promising, yet its practical application in salt lake brines is severely constrained by high Mg 2+ /Li + ratios. Herein, we unravel the intrinsic degradation mechanism by demonstrating that Mg 2+ intercalation weakens the Fe─O bonds in FePO 4 , triggering Fe 2+ dissolution, and inducing the formation of Fe 2 O 3 surface species that deteriorate electrode performance. To tackle this critical challenge, we propose a novel pulsed charging protocol where reverse potentials not only efficiently flush out surface‐intercalated Mg 2+ but also enrich Cl − on the electrode surface, thereby modulating the electronic structure and lowering the Mg 2+ deintercalation barrier. The FePO 4 ||Ag cell integrated with this protocol exhibits superior Li + extraction kinetics (∼20 mg g −1  h −1 ), high LiCl product purity (99.95%), and exceptional cycling stability (800 h operation with ∼80% performance retention) in real salt lake brines. This work establishes a theoretical basis for electrode protection and provides a robust strategy to advance high‐efficiency electrochemical lithium extraction.

Deep survival analysis in multimodal medical data: a parametric and probabilistic approach with competing risks

Implementation research to develop and optimize delivery models for evidence-based anemia control interventions in India: Protocol for the precision-driven response for anemia control and sustainable health (PRAKASH) study

Background Government of India launched Anemia Mukt Bharat (AMB) program in 2018. Despite its implementation, the prevalence of anemia remains high. To address the gaps in coverage, adherence and effective implementation, the Indian Council of Medical Research (ICMR) initiated the Pr ecision driven R esponse for A nemia C ontrol a nd S ustainable H ealth (PRAKASH) study. It is an implementation research study aimed at co-developing and optimizing a district-level model of evidence-based anemia control interventions. The intended goal is to reduce anemia prevalence to 20% or lower through scalable, context-specific, and sustainable strategies. Methods This multi-site study targets six key population groups: children (aged 6–59 months and 5–9 years), adolescents boys and girls (10–19 years), women of reproductive age (20–49 years) and pregnant women. Interventions are anchored in six pillar i. Test–Treat-Track until resolution, ii. prophylactic iron and folic acid supplementation, ensuring high complianceiii. anemia-relevant health interventions, iv. fortified rice distribution, v. dietary diversification and promotion of iron-rich foods, and vi. behavior change communication through Jan Andolan. Anemia prevalence will be estimated through a series of community-based cross-sectional surveys. Mixed methods approach will be employed to assess barriers and enablers at the individual, household, facility, and community levels. The study will utilize implementation science frameworks, including Consolidated Framework for Implementation Research (CFIR) and Expert Recommendations for Implementing Change (ERIC), to guide iterative implementation, real-time model refinement, and monitoring of performance. Information on outcome indicators will be collected to evaluate anemia prevalence, fidelity, feasibility, and service delivery improvements. Conclusion The study will try to readdress the implementation challenges across diverse regions and aims to develop a comprehensive and replicable model for the AMB 2.0 program. The study is likely to contribute to the global evidence base on implementation science for anemia reduction in low- and middle-income countries.

Interfacial Ru–C Coupling Harnesses Photoexcited Hot Electrons to Sustain Oxygen Cycling in Photothermal Methane Dry Reforming

ABSTRACT Methane dry reforming (DRM) remains challenged by the difficulty of simultaneously achieving high activity and long‐term stability under moderate‐temperature operation. Here we report sub‐2 nm Ru clusters anchored on chemically stable multi‐walled carbon nanotubes (Ru/MWCNTs) as a non‐oxide photothermal platform for efficient and durable DRM below 600 °C. Strong Ru–C π–d coupling delocalizes interfacial electrons and stabilizes metallic Ru, enabling broadband generation and interfacial utilization of photoexcited hot electrons under illumination. The resulting interface selectively activates CO 2 to generate highly labile Ru–O* species and sustains a fast, fully reversible Ru 0 /Ru–O cycle without relying on lattice oxygen from an oxygen‐carrier support. Rapid interfacial oxygen turnover promotes an oxygen‐assisted CH 3 O* pathway, delivering a turnover frequency of 25 s −1 . Consequently, Ru/MWCNTs achieves CO and H 2 formation rates of 632 and 526 mol g Ru −1 h −1 , respectively, a light‐to‐fuel efficiency of 25.4%, stable operation for 120 h, and robust activity across a wide CH 4 /CO 2 feed‐ratio window (0.43–2.33). Isothermal monochromatic irradiation together with operando, electronic, and kinetic analyses identify Ru–C interfacial coupling, rather than metallicity alone, as the key origin of the photothermal enhancement.

Decoding concealed information using multimodal neurophysiological signals

Traffic condition prediction for highway within work zones under dynamic traffic organization changes

In the context of sustainable transportation development, reducing carbon emissions, energy waste, and noise pollution caused by traffic congestion has become an urgent task for achieving environmental and social sustainability. The key to this goal lies in mitigating and preventing traffic congestion, for which high accuracy traffic condition prediction models serve as essential tools. During the reconstruction and expansion of highways and urban arterial roads, frequent adjustments to traffic organization and changes in geometric alignment introduce dynamic and uncertain characteristics into the traffic system. Existing methods struggle to accurately predict traffic conditions in the modified sections. To address this challenge, this study proposes a Dynamic Bayesian Graph Convolutional Neural Network (DBGCN). The model incorporates road geometric parameters and dynamic traffic organization changes as key inputs. It employs a Dynamic Bayesian Network (DBN) to model multi-source dynamic information and infer a dynamic adjacency matrix that reflects latent spatiotemporal dependencies between nodes. This dynamic adjacency matrix is then input into a Graph Convolutional Network (GCN), which fuses spatiotemporal features with traffic flow data to achieve accurate traffic conditions prediction for upgraded sections. Validation on the Wuxuan highway demonstrates that the proposed method outperforms benchmark models in traffic conditions prediction accuracy and produces traffic conditions propagation diagrams with high interpretability.

Cation–Anion Redox Co‐Modulation: Unlocking the Potential of All‐Electrochem‐Active Sulfur‐Based Solid‐State Batteries

ABSTRACT The capacity utilization of all‐solid‐state sulfur cathodes reveals a significant disparity between material and electrode levels due to the high proportion of inactive components required for electro‐ionic transport. While the all‐electrochem‐active (AEA) electrode concept seeks to bridge this gap, fully realizing the energy‐density potential of sulfur‐based cathodes remains challenging. Here, we report a new strategy for co‐modulating the redox of the transition‐metal cation/sulfur anion to unlock the potential of the sulfur‐based electrode. By carefully adjusting the coordination between S anions and Ti cations, we constructed the AEA electrode with S‐anion (TiS x , x > 2)/Ti‐cation (amorphous TiS 2 ) co‐redox, where TiS x activates the redox activity of sulfur‐rich phases with narrower bandgaps through the reversible cleavage and recombination of S–S bonds, thereby enhancing the capacity utilization of anion‐redox in the electrode level, and amorphous TiS 2 serves as an electrochemically active matrix facilitating mixed ionic‐electronic conduction. This design eliminates inactive components and enables synergistic anion‐cation redox chemistry. Consequently, this designed cathode achieves an unprecedented electrode‐level energy density of 1829 Wh/kg, sustains an areal capacity of 11.6 mAh/cm 2 , and exhibits long‐term stability over 10 000 h. Device‐level demonstrations validate this synergistic approach as an effective design principle for realizing high‐energy‐density, long‐life all‐solid‐state battery cathodes under practical conditions.

Exploring the repeatability of curved gait during an instrumented figure of 8 walk test, a new methodology

Abstract There is currently no consensus on how to assess curved gait. One of the challenges is to define when a turn begins and ends. Two fundamental characteristics of a turn are the variation in gait speed and the vertical rotational rate of the pelvis. Our study aimed to assess the repeatability of an instrumented figure of eight walk test (F8Wi) by evaluating right and left turns over 3 sessions. Fourteen healthy participants were included. Participants walked barefoot and with shoes, both at comfortable and fast speed. Several points of interest were identified: the start of speed deceleration (A), the start of body rotation (B), the peak angular acceleration (C), the end of body rotation (D), the end of speed acceleration (E). The dependent variables were time intervals between AB, AC, AD, AE, BD, and gait parameters measured during the turn defined by time between A and E: mean sum load from the lumbar inertial measurement unit, mean sum load from the lumbar inertial measurement unit normalised by AE time and mean of the right knee maximum flexion of each step during the turn. The coefficient of variation (CV), standard error of measurement (SEM), minimum detectable change (MDC 95% ), and 3D Bland‒Altman plot assessed repeatability. For all conditions, the CVs of time between AE, AD, and BD were less than 10%, whereas for times at AB, AC, AD, BD, the CVs were between 10% and 20%. The SEMs remained low for all variables and conditions, and the MDC95% was greater than the standard deviation. For the gait parameters, CVs were less than 10%, the SEMs remained low, and the MDC 95% was greater than the standard deviation. The protocol employed a combination of knee electrogoniometers, an IMU and a Doppler radar to evaluate curved walking during an F8Wi modified. Curved walking repeatable over time and under different gait conditions. This repeatability study shows that a more accessible setup could open the door to assessing curved walking.

Analysis of status and influencing factors of knowledge, attitudes, and expectations towards assisted reproductive technology among infertile women in Lebanon: A cross-sectional study

Background Infertility affects millions globally, and while assisted reproductive technology (ART) has become a cornerstone of its treatment, there is limited research on how Lebanese women perceive these services. This study aims to fill the gap by assessing each of the knowledge, attitudes, and expectations of Lebanese women experiencing difficulty conceiving towards ART. Methods A cross-sectional study was conducted between June and September 2024, involving 346 Lebanese women from two fertility centers in Beirut. Participants were selected by simple random sampling to complete the questionnaire through individual interviews. Descriptive and bivariate analyses were performed, and generalized linear models were used to explore the associated factors of knowledge, attitudes, and expectation scores. Results The results showed that 56.4% of participants had good knowledge, 54.9% had positive attitudes, and 73.7% exhibited high expectations. The generalized linear models revealed that previous ART use (β = 0.151), receiving ART information from a doctor (β = 0.064), female age (β = 0.005), and physical exercise (β = 0.119) were linked to higher knowledge, while higher family income (β = −0.133), history of immunodeficiency (β = −0.275), living in centers (β = −0.066), were linked to lower knowledge. For attitudes, residing in South Lebanon (β = 6.136), having a history of ovarian cyst removal (β = 2.065), receiving ART information from a doctor (β = 1.151) and female age (β = 0.120) were linked to positive attitudes, while living in centers (β = −1.835), having a regular menstrual period (β = −1.521) were linked to lower attitudes. For the expectations, female hormonal disorder (β = 2.758) was significantly associated with higher expectations, while advanced female age (β = −0.768) was associated with lower expectations. Conclusion This study identifies associated factors influencing knowledge, attitudes, and expectations toward ART. To optimize these aspects, interventions should focus on effective education, personalized treatments, reducing barriers, and providing psychological support to improve women’s reproductive health. Further research is needed to explore these factors in the broader population.

Chemo‐, Diastereo‐, and Enantioselective Radical Cyclization of Enals to Access Cyclopentanols Bearing an α‐Quaternary Center

ABSTRACT Controlling both chemo‐ and stereoselectivity in reactions of sterically demanding radicals remains a fundamental challenge in the construction of vicinal stereocenters, particularly those bearing an α‐quaternary carbon center. Despite their synthetic value, asymmetric radical cyclization of bulky carbon‐centered radicals onto enals to access α‐quaternary cyclic alcohols remained undeveloped. Here we report a stereoconvergent asymmetric radical alkylation/cyclization cascade of enals with unactivated, sterically congested alkyl iodides, enabled by an earth‐abundant chromium catalyst supported by a chiral bisoxazoline ligand. This method provides direct access to cyclopentanol and 2,3‐dihydro‐1 H ‐inden‐1‐ol derivatives bearing an α‐quaternary stereocenter in good yields with excellent diastereo‐ and enantioselectivities (>20:1 d.r., up to 99% ee). The reaction proceeds under mild conditions, exhibits broad functional‐group tolerance, and completely suppresses the competing direct coupling of alkyl electrophiles with aldehydes. Experimental and DFT studies support a catalytic manifold in which a chiral Cr(II) complex mediates single‐electron generation of alkyl radicals for addition to enals and controls the enantiodetermining outer‐sphere cyclization of the resulting bulky tertiary radical onto the carbonyl group.

Synthesis of chitosan-starch@Fe2O3 nanocomposite for efficient adsorption of methyl orange and methylene blue dyes from aqueous solutions

Developmental organization of sensory and sympathetic ganglia

AI and social science: Automatic classification tools for big data analysis in sociological research

This study examines the use of Social Network Sites for public institutional communication through a sociological, data-driven lens, focusing on the challenges and potential of automated classification tools for data analysis. Although Large Language Models are increasingly used to process social media data, a key research gap remains: few studies systematically assess whether AI-based categorizations are as reliable as human coding, especially when categories are semantically ambiguous. The research addresses the following questions: How reliable are AI-generated classifications compared to those made by human experts? Is human–machine agreement comparable to the level of agreement observed among human coders? To experimentally test this approach, we conducted a case study on Facebook posts published by two Italian universities (March 2020–March 2023), classified into eight categories of public institutional communication. Three researchers independently annotated the dataset. Human annotations are used as a benchmark to assess agreement patterns and to compare them with classifications produced by AI-based systems. Results show substantial interpretive ambiguity across several categories, mirrored by variability among human coders. Nonetheless, automated models achieve agreement with human classifications that is broadly comparable to inter-coder agreement. Overall, the findings support integrating AI as an additional coder within hybrid workflows to enable scalable and transparent sociological analysis of complex social media data.

Tailoring the Cu Local Microenvironment to Create Formate Conversion‐Desorption Equilibrium for Industrial Level Formaldehyde Electrooxidation

ABSTRACT Copper‐based electrocatalysts exhibit high initial activity for formaldehyde oxidation but suffer rapid deactivation under industrially relevant current densities. While this instability has traditionally been attributed to copper self‐oxidation, the fundamental origin of performance decay remains debated. Here we show that the persistent accumulation of formate intermediates rather than Cu oxidation constitutes the primary deactivation pathway, and that this bottleneck can be overcome by establishing a dynamic conversion‐desorption equilibrium through local electronic microenvironment engineering. We develop an AgCu microsphere catalyst (AgCu‐MSs/CF) with an optimized Ag loading of 4.1 wt.%, which achieves a current density of 1068 mA cm −2 at 1.0 V versus RHE and operates stably up to 1.3 V versus RHE, well beyond the thermodynamic oxidation limit of Cu. Combined in situ spectroscopy and density functional theory reveal that Ag incorporation induces electron transfer from Ag to Cu, downshifting the d‐band center of Cu. This electronic modulation weakens formate adsorption, thus preventing active‐site blocking and ensuring the sustained catalytic activity. Leveraging this design principle, a bipolar FOR||HER electrolyzer operates stably for over 312 h. This work establishes that engineering the dynamic equilibrium between intermediate formation and removal offers a generalizable strategy to mitigate poisoning in Cu‐based catalysts for complex oxidation reactions.

Nonlinear control design for stabilization and tracking of a rotary inverted pendulum

Circ-EnviroPredict: A machine learning-based tool to predict potential involvement of circRNAs with cold and drought stress through a Word2Vec approach

Circular RNAs (circRNAs) are a class of RNAs characterized by a covalently closed loop structure formed between the 5’ and 3’ splice sites. Over the years, circRNAs have been shown to act in post-transcriptional regulation and as potential sponges for miRNAs. Although the number of circRNAs identified has grown and several databases have been developed, challenges persist in analyzing large datasets and extracting molecular information. Machine learning-based tools have been gaining attention due to their potential to massively analyze molecules and understand patterns in response to stresses. However, there is still a lack of studies that connect circRNAs and abiotic stress using this approach. In this study, we developed circ-EnviroPredict, a tool designed to predict the potential involvement of circRNAs in cold and drought stress conditions based on biological sequence data. Using a Random Forest–based methodology, the circ-EnviroPredict tool was trained using rice and maize circRNA sequence data, in which k-mers were transformed into vector representations using the Word2Vec approach. Using independent test sets, it was possible to obtain accuracy values ~77% and ~81% forcold and drought models respectively. It was also possible to validate using data from other plant species, including Arabidopsis thaliana, Glycine max and Triticum aestivum . In addition, a k-mer density analysis revealed an enrichment of AT-rich motifs in circRNAs associated with abiotic stress conditions, providing biological insights into the sequence patterns captured by the predictive models. These results provide insights into how machine learning and Word2Vec techniques can be used to classify the potential involvement of plant circRNAs under abiotic stress conditions using biological sequence data.

Carrier‐Dynamics‐Regulated BiOI/Au/TiO ₂ Z‐Scheme Photoanode for Selective Glycerol Photoelectrooxidation and Mass‐Transfer‐Enhanced Continuous‐Flow Operation

ABSTRACT Selective conversion of biomass‐derived glycerol into high‐value chemicals is challenged by poor selectivity and mass‐transfer limitations. Here, a BiOI/Au/TiO 2 photoanode is developed, where plasmonic Au mediators facilitate a transition from Type‐II to Z‐scheme charge transfer. This heterojunction preserves strongly oxidative holes on the TiO 2 surface, as confirmed by femtosecond transient absorption spectroscopy and spatially resolved MnO x photodeposition. In situ characterizations and density functional theory (DFT) reveal that this hole‐rich interface strengthens specific primary hydroxyl (pri‐OH) adsorption, lowering the rate‐determining dehydrogenation barrier to ∼0.6 eV. It also promotes rapid glyceraldehyde (GLAD) desorption, suppressing over‐oxidation. In a static H‐cell, the photoanode achieves 87% GLAD selectivity with a glycerol conversion rate of 341.25 mmol·m −2 ·h −1 . To overcome diffusion limitations, computational fluid dynamics (CFD) simulations were employed to design a continuous‐flow reactor for the 100 cm 2 large‐area photoanode. The flow system prevents product accumulation, boosting GLAD selectivity from 48% (H‐cell) to 77% for the large‐area electrode, with enhanced glycerol conversion (60.34%) and stable 120 h operation. This work provides a laboratory scale‐up by integrating nanoscale reprogramming with macroscale reactor engineering.

The infraslow fluctuation of sigma power during sleep and its links to markers of arousal and memory reactivation across development

Abstract Sleep is both a state of disconnection from the environment and a critical period for restoration. But how does sleep balance responsiveness with the protection of key functions? The infraslow fluctuation of sigma power (ISFS)—the clustering of sleep spindles over 10–100 s—is thought to regulate this trade-off in rodents by alternating between fragile periods of high arousability and protected periods of spindle-rich stability. However, the organization of arousal and memory reactivation markers within the human ISFS and its conservation in younger ages remain unclear. This study characterizes the ISFS from childhood to young adulthood ( N  = 154; ages 8–26), examining its relationship with electrophysiological markers of arousal (i.e., microarousals) and memory reactivation (i.e., slow wave-sleep spindle coupling). Results indicate that the ISFS is present across all ages, with frequency, variability, and strength increasing from early to late adolescence. Notably, electrophysiological markers of arousal and memory reactivation are organized within the spindle-rich ISFS peak. The consistent presence and organization of the ISFS suggest it may be intrinsic to sleep, with adolescence marking a dynamic window. These insights may guide interventions to promote healthier sleep across development.

Correction: Protocol for the development of a procedure guide on Laparoscopic Cholecystectomy: Beyond bile duct injury prevention