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Molecular dynamics investigation of interfacial energy and mechanical behavior in braid-reinforced hollow fiber membranes
Abstract This study presents a molecular dynamics investigation of braid-reinforced hollow fiber membranes to elucidate the interfacial and mechanical behaviors of polymeric composites composed of cellulose acetate (CA) and polyacrylonitrile (PAN). The analysis focuses on three representative configurations, homogeneous (CA/CA-II), semi-heterogeneous (PAN|CA/CA-I), and heterogeneous hybrid (CA/PAN-III), to evaluate their interfacial energies, adhesion mechanisms, and tensile responses. The calculated interfacial energies of − 6.32077, − 5.69262, and − 4.71113 mJ/m 2 for CA/CA-II, PAN|CA/CA-I, and CA/PAN-III, respectively, reveal that chemical homogeneity promotes stronger interfacial bonding, whereas polarity mismatches between functional groups (–OH, –OCOCH 3 , and –CN) weaken adhesion and increase diffusivity at the interface. Mechanical testing through MD tensile simulations further demonstrates that the CA/PAN-III composite exhibits pronounced stress fluctuations and higher local interfacial activity. At the same time, the CA/CA-II system maintains the highest cohesive stability and elastic modulus due to structural uniformity. The CA/PAN-III hybrid achieves an optimal balance between flexibility and strength, indicating its suitability for water treatment membranes that require both mechanical resilience and interfacial durability. These findings provide molecular-level insight into how polymer compatibility governs the performance of braid-reinforced hollow fiber membranes and offer valuable guidelines for designing next-generation high-strength composite membranes.
Comparative repair efficacy of three mesenchymal stem cell sources in rat full-thickness talar cartilage defects
Benzene Ring Expansion to Borepins via Borylene Insertion Enabled by Thorium–Arene Cooperativity
Machine learning reveals mechanisms and feedstock effects on potassium and magnesium uptake by wheat in biochar-amended soil in greenhouse
Comparison of neural thickening detected by clinical palpation and ultrasonography in patients from the Brazilian Amazon
Two-Dimensional Chiral Perovskites for Integrated High-Performance Ultraviolet Full-Stokes Polarization Detection
Acquisition dynamics of ‘Candidatus Liberibacter asiaticus’ and ‘Ca. L. americanus’ by Diaphorina citri highlights the epidemiological decline of ‘Ca. L. americanus’
Metabarcoding assessment of the diet of an introduced continental lizard to an oceanic island reveals dietary niche conservatism
Abstract Invasive species can have devastating effects when introduced into remote island ecosystems, and a fundamental aspect of this concerns the diet of these exotic taxa. Here, we employed a DNA metabarcoding approach to determine the diet of the lizard Agama picticauda on Réunion Island, where it was introduced in 1995. Two separate markers were used to identify dietary components: COI for animals and trnL for plants. The arthropod aspect was notably conservative, with the agama continuing to predominantly consume ants, as they do in their native range. A variety of other invertebrates were also preyed upon, the vast majority being introduced species. For plants, again a wide variety was detected, and while most could not be identified fully, it seems that agamas are deliberately consuming many species, rather than accidentally ingesting them along with targeted invertebrates. Agamas may play a role in seed dispersal of invasive plant species. We also detected some nematode groups, although with limited comparative sequences, these could not be identified to the species level. Several invertebrate records appear to be new for Réunion Island, highlighting how reptiles can be considered as excellent biodiversity samplers, with barcoding diet studies providing novel data on poorly known invertebrate groups. The minimal identification of endemic prey items may reflect the fact that agamas are still predominantly occupying anthropogenically disturbed parts of the island. Our study therefore provides baseline data that can be used to determine the impact of this introduced lizard as it spreads through the ecosystem.
Interphasial Catalytic Anion Reduction for Stable Anode-Free Sodium-Metal Batteries
Plasma-driven formation of vertically aligned silver–phosphorus core–shell nanostructures
Direct Access to Chiral Tertiary Alcohols via Copper-Catalyzed Enantioconvergent <i>O</i> -Alkylation of Water with Racemic α-Tertiary Haloamides
Automated body composition quantification from non-contrast CT for urolithiasis classification and exploratory incident risk assessment
Distributed MAC scheduling in IEEE 802.15.7-oriented VLC networks via federated deep reinforcement learning
Abstract Visible light communication (VLC) networks modeled through a scheduling-level IEEE 802.15.7-oriented PHY/MAC abstraction are sensitive to line-of-sight blockage, receiver orientation, ambient-light noise, heterogeneous traffic loads, and inter-luminaire optical interference, which limits the effectiveness of fixed medium access control (MAC) policies in dense deployments. This study presents a federated deep reinforcement learning framework for distributed MAC scheduling in multi-luminaire IEEE 802.15.7-oriented VLC networks. Each luminaire is modeled as a local scheduling agent that selects the served receiver, transmission slot, optical power level, and physical-layer (PHY) mode from local queue, channel, blockage, interference, and illumination states. Instead of sharing raw observations, luminaires periodically exchange model parameters with a federated aggregation server to coordinate policy updates while preserving data locality. The proposed method is evaluated in a custom discrete-time simulator for a $$5\times 5\times 3~\textrm{m}^3$$ indoor VLC scenario with four ceiling luminaires, 8–32 receivers, stochastic traffic arrivals, receiver mobility, ambient-light noise, and line-of-sight blockage. Results averaged over 30 independent runs show that, at $$K=32$$ receivers, the proposed scheduler reduces average packet latency from 45 ms to 31 ms and 95th-percentile latency from 92 ms to 66 ms relative to the implemented resource-constrained centralized deep Q-network (DQN) baseline. Under a blockage probability of 0.3, the packet delivery ratio increases from 0.858 to 0.902, while under high ambient-light noise the packet error rate decreases from 0.064 to 0.049. The method also achieves a Jain fairness index of 0.96, reduces average synchronization overhead from $$18.5\%$$ to $$4.7\%$$ at a synchronization interval of 10 episodes, and shortens convergence time from 940 to 670 episodes at $$N=9$$ luminaires. Illumination and flicker diagnostics show that executed actions satisfy the normalized feasibility mask after filtering. These results indicate that, within the adopted IEEE 802.15.7-oriented simulation abstraction, periodic federated parameter sharing improves the scheduling trade-off by reducing delay and coordination cost while preserving mask-enforced lighting feasibility, improving empirical reliability and fairness, and showing favorable multi-luminaire scalability trends.
Timestep-conditioned Attention and Multi-dimensional Evidence framework for efficient multimodal chest X-ray anomaly detection
Cellular and subcellular localization of the copper transporter CTR1 in human postmortem hippocampus and striatum
Vision expert guided inspection for industrial anomaly detection
Industrial anomaly detection (IAD), aiming at automatically identifying abnormal patterns that deviate from the normal manufacturing process, plays a critical role in ensuring product quality and equipment safety for intelligent manufacturing systems. In this work, we delve into exploring the generalized and subtle-pattern awarded defect detection. We also propose a visual expert-guided multi-scale anomaly detection method. As the extracted regions often exhibit subtle and vague features that hamper the precise and reliable detection, we leverage the established super-resolution technique to enhance the spatial resolution and recover fine-grained details. It facilitates more discriminative defect representation and improves the model’s capacity at localize anomalies at finer scales. The multi-scale fusion module is constructed by the graph attention network. It aggregates the suspicious regions across different scales by modeling their inter-scale dependencies and contextual relationships. As it dynamically weights and localities those features, it preserves both the micro irregularities and macro structural deviations, hence offering comprehensive anomaly information. Extensive experiments under zero-shot and few-shot settings were conducted on several public datasets. The results demonstrate that the proposed method consistently outperforms existing mainstream approaches in both image-level and pixel-level anomaly detection, achieving pixel-level values of 98.6% and 98.1% under the 4-shot setting on two major benchmarks, and 94.6% under the zero-shot setting, with particularly strong capability in detecting subtle defects on fine-grained textures. It also exhibits enhanced robustness and generalization in cross-domain transfer scenarios.
Retraction Note: Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images
C═C/N═O Metathesis Enables Oxidative Decarboxylation
A novel design using a virtual control group to evaluate non-inferiority of nevirapine and lamivudine dual maintenance in HIV therapy
Purpose Reducing the number of drugs in combined antiretroviral therapy (cART) likely reduces toxicity. We hypothesized that dual therapy (DT) with nevirapine (NVP) and lamivudine (3TC) would be non-inferior to a virtual control without treatment failure. Methods This multicenter study enrolled patients on cART with HIV plasma viral load (pVL) <50 cp/ml for ≥2 years and on NVP for ≥6 months. Patients were compared to a simulated virtual control group with an assumed failure rate of zero. Those with prior Non-Nucleoside Reverse Transcriptase Inhibitor failure or 3TC resistance were excluded. Treatment was simplified to DT with NVP/3TC for 48 weeks, with quarterly pVL-measurements. The primary endpoint was confirmed virologic failure (pVL ≥ 200 cp/mL). A 4% non-inferiority margin and sample size of 201 were set, with a stopping rule if three virologic failures occurred. Results From April 2019 to January 2023, 201 patients from five centers in Switzerland and Germany started DT, which 194 participants completed. Two patients (1.03%, 95% CI: –0.92% to 3.68%) reached the primary endpoint for failure due to adherence issues. No additional failures were observed during a 12-month post-study follow-up of 184 participants. Conclusions Simplification to NVP and 3TC was as effective as the ideal virtual control. However, the results of NVP and 3TC maintenance therapy are only applicable to people living with HIV who meet the study’s inclusion and exclusion criteria. Virtual controls could improve research efficiency and warrant further evaluation.