Browse Articles
Discover research articles across all indexed journals
Development and validation of a nomogram for predicting pulmonary embolism recurrence using muscle and fat parameters
Towards universal multi-dimensional parallelization communications by direct diverse fiber/3D/2D chip hybrid integration
Correction: NAxtra magnetic nanoparticles for low-cost, efficient isolation of mammalian DNA and RNA
Meta-analyses on charitable giving clarify evidence for empathic and effective altruism
Surface integrity and kerf quality improvement in laser beam machining of Nimonic C-263 by hybrid TOPSIS–grasshopper optimization approach
Optical control of the cardiac rhythm with photoswitchable NaV1.5 channel blockers
Development and validation of a risk classification integrating the location index to predict renal function after robotic partial nephrectomy
Dynamic stabilization of a mechanical oscillator in the absence of any stable feature
Abstract How and why base vibration can stabilize an inverted pendulum has puzzled the scientific community for decades, until the work on dynamic stabilization by Pyotr Kapitza pointed at the alternation between unstable and stable modes as a pathway to stability. We report the discovery of a mechanical oscillator that switches between two unstable modes, has an unstable average, and, yet, can be dynamically stabilized. Our system is governed by a modified Meissner’s model – a one-degree-of-freedom oscillator where both stiffness and damping are modulated through a square wave to switch between positive and negative values. We theoretically prove the existence of compact antiresonance windows and provide experimental evidence through a cantilever beam oscillator subject to magnetic and aerodynamic forcing. The prospect of dynamic stabilization in the absence of any stable feature has vast implications from network dynamical systems, to structural mechanics and robotics.
Waste-derived nano-Al₂O₃-loaded pyranopyrazole composite for high-capacity cadmium and methylene blue removal with mechanistic and DFT validation
Abstract Upcycling industrial aluminum waste into functional adsorbent materials offers a sustainable strategy for addressing complex wastewater contamination by coexisting organic dyes and toxic heavy metals. In this study, a waste-derived nano-Al₂O₃-loaded pyranopyrazole composite is rationally designed to enable high-capacity and simultaneous removal of methylene blue and Cd(II) ions from aqueous systems. The heteroatom-rich pyranopyrazole framework provides multiple organic binding domains, while nano-alumina incorporation introduces additional Al–O active sites and surface hydroxyl groups that collectively enhance interfacial adsorption efficiency. Comprehensive structural, spectroscopic, and microscopic characterization confirms homogeneous alumina anchoring and the formation of a stable organic–inorganic hybrid interface. The resulting composite exhibits markedly improved adsorption performance relative to the pristine pyranopyrazole, achieving maximum experimental uptake capacities of 189 mg g⁻¹ for methylene blue and 343 mg g⁻¹ for Cd(II) under optimized conditions. Adsorption kinetics follow a pseudo-second-order model, indicating surface-controlled chemisorption, while equilibrium data are best described by the Freundlich isotherm, reflecting adsorption on energetically heterogeneous surfaces. Thermodynamic analysis reveals that both adsorption processes are spontaneous and exothermic. Spectroscopic evidence combined with density functional theory calculations demonstrates that adsorption proceeds through synergistic coordination, electrostatic attraction, hydrogen bonding, and π-interactions involving nitrogen- and oxygen-donor sites of the organic framework together with alumina-derived Al–O centers. The composite maintains high removal efficiency over repeated adsorption–desorption cycles and exhibits effective performance in real wastewater matrices. Overall, this work establishes a clear structure–interaction–performance relationship for waste-derived alumina–organic hybrids and highlights their potential as sustainable, reusable, and multifunctional adsorbents for advanced water remediation targeting both organic dyes and heavy-metal contaminants.
Alzheimer’s Aβ catalyzes Tau phase separation and aggregation via early nanocluster solubilization
Abstract Extracellular amyloid-beta (Aβ) plaques and intracellular neurofibrillary tangles (NFTs) composed of hyperphosphorylated Tau are the two main pathological hallmarks of Alzheimer’s disease (AD). Although the co-occurrence and synergistic effects of Aβ and Tau are well established, the mechanisms underlying their interplay in a biomolecular condensate environment remain unclear. Here we show that Aβ40 does not undergo liquid–liquid phase separation (LLPS) but significantly enhances Tau phase separation and is recruited into Tau condensates. This recruitment alters condensate physicochemical properties, accelerates liquid-to-solid maturation, promotes Tau amyloid fibril formation, and increases Tau-mediated cytotoxicity. Notably, prior to condensate formation, Aβ40 transiently solubilizes Tau nanoclusters into smaller species. Simulations further indicate that early interactions are non-specific and mediated by Tau repeat domains, ultimately promoting pathogenic aggregation. These findings support a model wherein Aβ act as a catalyst for Tau condensation and fibrillation towards pathological aggregates by solubilizing Tau nanoclusters during early phase interactions.
Study on freeze–thaw cyclic durability of reclaimed ceramic concrete in western high altitude region
Abstract To achieve the resource utilization of waste ceramic particles and promote the application of recycled concrete in cold regions, this study systematically investigates the effects of waste ceramic particle replacement ratios on the freeze–thaw resistance and service reliability of recycled concrete. Six groups of specimens with ceramic particle replacement ratios of 0%, 20%, 40%, 60%, 80%, and 100% were designed. Freeze–thaw cycle tests were carried out using the rapid freezing method, with mass loss rate and relative dynamic elastic modulus as the key evaluation indicators to determine the optimal mix proportion. Scanning electron microscopy (SEM) was used to observe the evolution of the microstructure, and a reliability function was established based on the Wiener distribution probability model to predict the remaining service life of the optimally proportioned specimens. The results show that during freeze–thaw cycles, the relative dynamic elastic modulus of all specimens decreases continuously, while the mass loss rate exhibits a variation trend of initial increase followed by subsequent decrease. The specimens with a 20% ceramic particle replacement ratio demonstrate the best freeze–thaw resistance, failing only after 398 freeze–thaw cycles. Microstructural analysis reveals that at early freeze–thaw cycles, the damage is dominated by the deterioration of the surface interfacial transition zone (ITZ), whereas at high cycles, severe degradation occurs, including pore connectivity and the collapse of the cementitious network formed by hydration products. When the ceramic particle replacement ratio exceeds 20%, the freeze–thaw resistance of the specimens decreases significantly. This study reveals the freeze–thaw deterioration mechanism of recycled concrete incorporating waste ceramic particles as aggregates, and the findings provide a theoretical basis and technical support for the mix optimization and engineering application of such recycled concrete in cold regions.
In situ synchrotron X-ray scattering reveals organic-mediated scaling mechanisms on desalination membranes
Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks
Abstract Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning–optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (biHOW) for feature selection and its continuous counterpart, iHOW , for hyperparameter tuning. Both variants emulate human cognitive phases—data absorption, information analysis, reinstitution, and adaptive knowledge development enabling efficient traversal of complex search spaces. Using the Multi-Scale Attention Network (MSAN) as the forecasting backbone, which is well suited for modeling renewable energy time series due to its ability to capture multi-scale temporal dependencies ranging from short-term fluctuations to long-term seasonal patterns, the proposed framework achieved high accuracy for wind and solar generation prediction. The MSAN model attained Mean Squared Errors (MSE) of 0.0105 for wind and 0.0976 for solar forecasting. Applying biHOW for feature selection reduced the average misclassification rate to 0.3925 (wind) and 0.4161 (solar) while identifying compact, interpretable feature subsets. The iHOW optimizer further fine-tuned architectural and training parameters, decreasing MSE to $$1.10883\times 10^{-6}$$ for wind and $$7.08664\times 10^{-6}$$ for solar, outperforming state-of-the-art metaheuristics including HHO, GWO, PSO, and JAYA. These findings demonstrate the effectiveness of iHOW-based optimization in enhancing forecasting accuracy and computational scalability. The proposed hybrid framework supports adaptive forecasting for intelligent energy management within modern smart grids.
Ultra-efficient physical field computing by complex-valued network quantization
Development of β-CD metal organic frameworks loaded with olaparib: a novel approach for the treatment of cervical cancer
The role of amygdala GABA neurons in controlling stress and reproduction in female mice
Abstract Stress can disrupt menstrual cycles, impair fertility and cause reproductive disfunction. The posterodorsal medial amygdala (MePD) integrates stress signals and regulates the gonadotropin-releasing hormone (GnRH) pulse generator through a dense network of GABA and Urocortin-3 (UCN3) neurons, yet the mechanisms underlying the circuitry remain poorly understood. Here, we combine in vivo mini-endoscopic calcium imaging, optogenetics, clustering analysis, and computational modeling to investigate the MePD circuitry in female mice. We uncover two anti-correlated GABA subpopulations in the MePD that are involved in the response to restraint stress and UCN3 neuron stimulation. Computational modeling suggests that mutual inhibition between these GABA groups drives their anti-correlated activity and predicts how these interactions shape downstream responses to stimulation of GABA and UCN3 neurons. In vivo optogenetics confirms that GABA neurons are critical for transmitting UCN3 signals to regulate luteinizing hormone (LH) pulse frequency. Together, our findings reveal amygdala GABAergic circuit mechanisms that mediate stress effects on reproductive health, linking emotional processing and neuroendocrine control.
Quantum dynamics, master equation and equilibrium for a qubit coupled to a thermal boson field
Broadband multi-beam lens-assisted mmID enabling multi-gigabit backscatter data rates for next-generation wireless networks
Abstract The growth of next-generation Internet-of-Things (IoT) and digital-twin systems has created wireless environments where identification must sustain fiber-level data rates with low latency while operating at minimal energy cost and maintaining robust angular coverage. Conventional backscatter at microwave frequencies remains limited to megabit rates, and most millimeter-wave demonstrations operate with limited coverage. This work presents a lens-assisted millimeter-wave identification (mmID) system that unites multi-gigabit connectivity with wide solid-angle coverage. The design integrates a cross-polarized broadband antenna array with a dielectric lens, enabling multi-beam operation with angle-dependent modulation across ± 55° and a peak differential radar cross section of -13.4 dBsm. Demonstrated backscatter performance includes 4 Gbps 32-QAM at 5 m with an energy cost of 0.08 pJ bit −1 and 1 Gbps operation over 20 m. Link-budget analysis projects 1 Gbps backscatter ranges up to 2.6 km under the 75 dBm EIRP permitted in 5G millimeter-wave systems, establishing an energy-efficient pathway for high-capacity, long-range wireless identification.
Performance optimization of InSe-FETs using high-k dielectric materials for analog/RF applications
Abstract This study investigates the performance of Indium Selenide (InSe)-based field-effect transistors (FETs) incorporating high-k dielectric materials, using atomistic simulations based on the non-equilibrium Green’s function formalism. Initially, the effect of high-k dielectrics on the ON/OFF current ratio ( I ON / I OFF ) is analyzed to assess their suitability for digital applications. Subsequently, key analog and radio-frequency (RF) performance metrics, such as intrinsic gain ( A V ), transconductance generation factor (TGF), and cut-off frequency ( f T ), are examined. The results demonstrate notable enhancements: transconductance ( g m ) increases by 98.3%, TGF by 73.7%, and A V by 72.66%. However, a 9.85% reduction in f T is observed, indicating a trade-off between gain and speed. These findings highlight the potential of high-k dielectric engineering in optimizing InSe-based FETs for next-generation analog and RF circuits.