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Machine learning-enhanced entanglement characterization in Bi-partite Ququart systems
No full textWe present a systematic comparative analysis of machine learning and traditional approaches for quantum entanglement characterization in bi-partite ququart systems. Quantifying entanglement traditionally requires full quantum state tomography, necessitating measurements, where is the number of mutually unbiased bases, is the subsystem dimensionality, and is the number of identical copies. For ququart systems with , this translates to hundreds of distinct measurement settings, each requiring multiple copies ( typically in thousands)—resulting in hundreds of thousands of total measurements in practice. Our research demonstrates that neural networks achieve comparable or superior accuracy with orders of magnitude fewer measurement settings. We evaluate three architectures—Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Transformer—against Maximum Likelihood Estimation (MLE) and Bayesian methods across measurement counts ranging from 10 to 400. Neural approaches achieve up to 1650× faster computation times compared to traditional methods while maintaining competitive accuracy. At 100 measurements, the Transformer achieves Mean Squared Error (MSE) of , while MLE yields —over 10× higher error—despite taking 178 times longer. All neural methods in our study show error reduction scaling as approximately with increased measurements. While this observed scaling might be influenced by our specific architectures, it provides important practical guidance for experimental design. The Transformer architecture demonstrates exceptional sample efficiency, achieving with 100 measurements what traditional methods require 250-400 measurements to accomplish—a significant advantage for resource-constrained quantum experiments. This research provides a viable pathway for real-time entanglement characterization in higher-dimensional quantum systems where traditional methods become computationally prohibitive
Modeling and Estimation of Stiffness Imposed by Cable-Based Exosuits
No full textCable-based exosuits are one of the prevalent choices for rehabilitation, among others. These exosuits are redundant in nature and are capable of imposing a desired joint torque along with a specific impedance simultaneously due to redundancy. The exosuit imposed impedance majorly depends on the geometric routing of the cables and tension in cables. Therefore, the resulting stiffness and damping imposed onto humans could be different from what is intended for a musculoskeletal system of generalized anthropometry. Thus, modeling and estimating the impedance imposed by a cablebased exosuit is of utmost importance for the design and development of efficient and effective rehabilitation exosuits. The current work developed a framework to estimate stiffness imposed by a passive cable-based exosuit onto a human arm. To test the framework, an experiment was conducted on two participants, and the joint space and task space stiffness was estimated using the developed framework. The results indicate that during the design of routing, the geometry is more important than the cable tension. Furthermore, for the same cable routing, the imposed stiffness differed in magnitude for both participants. The developed framework can be even more beneficial in the design of complex and nonintuitive cable routing
Improving Streamflow Prediction Using Multiple Hydrological Models and Machine Learning Methods
No full textStreamflow prediction is crucial for flood monitoring and early warning, which often hampered by bias and uncertainties arising from nonlinear processes, model parameterization, and errors in meteorological forecast. We examined the utility of multiple hydrological models (VIC, H08, CWatM, Noah-MP, and CLM) and machine learning (ML) methods to improve streamflow simulations and prediction. The hydrological models (HMs) were forced with observed meteorological data from the India Meteorological Department (IMD) and meteorological forecast from the Global Ensemble Forecast System (GEFS) to simulate flood peaks and flood inundation areas. We used Multiple Linear Regression, Random Forest (RF), Extreme Gradient Boosting (XGB), and Long Short-Term Memory (LSTM) for the post-processing of simulated streamflow from HMs. Considering the influence of dams is crucial for the effectiveness of HMs and ML methods for improving streamflow simulations and predictions. In addition, ML-based multi-model ensemble streamflow from HMs performs better than individual models, highlighting the need for multi-model-based streamflow forecast systems. The post-processing of streamflow simulated by the hydrological models using ML significantly improved overall streamflow simulations, with limited improvement in high-flow conditions. The combination of physics-based hydrological models, observed climate data, and ML methods improve streamflow predictions for flood magnitude, timing, and inundated area, which can be valuable for developing flood early warning systems in India
Achieving superior strength and low mass density in a novel γ′ strengthened CoNi-based superalloy
No full textThe present paper reports the design of a new class of γ/γ′ containing high-strength CoNi-based superalloys in the Co–Ni–Al system by the addition of Ta and Ti. The designed alloys with compositions Co–30Ni–10Al–2Ta–xTi (where x = 0, 2, 4 at.%) exhibit a γ/γ′ microstructure suitable for moderate temperature applications (≤ 800 °C) requiring exceptional specific strength. The 3D APT compositional analysis of the γ and γ′ phases indicates a Co-rich matrix, while the ordered γ′ precipitates are Ni-rich with a stoichiometry of (Ni,Co)₃(Al,Ta). The subsequent addition of Ti up to 4 at.% increases the Ni partitioning to the γ′ precipitates, enhancing their stability. Furthermore, the addition of 4 at.% Ti increases the γ′ solvus temperature by 170 °C, from 950 to 1120 °C, and concurrently decreases the mass density from 8.50 to 8.34 g/cm3. A positive correlation between yield strength and temperature is observed in the Ti-containing alloys, with maximum yield strengths and specific yield strengths of 1025 ± 25 MPa and 122 MPa·g⁻1·cc, respectively, in the 2Ta2Ti composition, and 1030 ± 20 MPa and 123 MPa·g⁻1·cc, respectively, in the 2Ta4Ti composition. Long-term stability studies, including microhardness measurements and quantitative assessments of microstructural features, demonstrate excellent microstructural stability and resistance to coarsening in the 2Ta4Ti alloy at 900 °C. This work provides the opportunity to further design low-mass-density CoNi-based alloys with superior performance in the medium-temperature domain required for Advanced Ultra-Super-Critical (AUSC) power plants. This paper is part of a special volume in Journal of Materials Science in honor of Professor Kamanio Chattopadhyay, renowned for his research in our field and his significant contributions to the development of novel alloys for various engineering applications. In his early 60 s, Professor Chattopadhyay’s group pioneered the discovery of low-density Co-based superalloys with a γ/γ' microstructure closely resembling that of Ni-based superalloys. This work involves some of the alloy compositions we conceived during our doctoral thesis work with him
Neurotransmitter-Loaded DNA Nanocages as Potential Therapeutics for α-Synuclein-Based Neuropathies in Cells and In Vivo
No full textParkinson’s disease is one of the neuropathies characterized by accumulation of the α-synuclein protein, leading to motor dysfunction. Levodopa is the gold standard treatment; however, in long-term usage, it leads to levodopa-induced dyskinesia (LID). New therapeutic options are need of the hour to treat the α-synuclein-based neuropathies. The role of imbalance of neurotransmitters other than dopamine has been underestimated in α-synuclein-based neuropathies. Here, we explore the role of serotonin, epinephrine, and norepinephrine as a therapeutic moiety. For the efficient in vivo delivery, we use a DNA nanotechnology-based DNA tetrahedron that has shown the potential to cross the biological barriers. In this study, we explore the use of DNA nanodevices, particularly a DNA tetrahedron functionalized with neurotransmitters, as a novel therapeutic approach for MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced Parkinson’s disease in a PC12 cellular system. We first establish the effect of these nanodevices on the clearance of the α-synuclein protein in cells. We follow the study by understanding the various cellular processes like ROS, iron accumulation, and lipid peroxidation. We also explore the effect of the neurotransmitter-loaded nanodevices in an in vivo zebrafish model. We show that neurotransmitter-loaded DNA nanocages can potentially clear the MPTP-induced α-synuclein aggregates in cells and in vivo. The findings of these works open up new avenues for use of DNA nanotechnology by functionalizing it with neurotransmitters for future therapeutics in treatment of neurodegenerative diseases such as Parkinson’s disease
Transitioning Surface Wettability of Ti6Al4V via Laser Ablation and Post-processing Methods
No full textThe surface wettability of metals and alloys holds significant interest for industrial, commercial, and research applications. Laser-based texturing has emerged as a prominent technique for modifying wettability due to its precision, versatility, and automation compatibility. This study explores the wettability modification of Ti6Al4V alloy through laser ablation, followed by heat treatment and chemical coating. Initially, laser scanning speed and power are varied to create textured surfaces, which are then analyzed for feature dimensions using profilometry. Optimal parameters, determined as 9 mm/s scanning speed and 60 W power, yielded low-aspect-ratio features to enhance surface roughness and promote wettability. Using these parameters, three sets of laser-ablated samples were prepared in grid patterns with varying line spacing. Two of these sets then underwent post-processing: low-temperature heat treatment and hexadecyltrimethoxysilane (HTMS) coating. Contact angle (Ɵ) results revealed that Ɵ decreased from 72.4 ± 3.1° on the untreated surface to 34.5 ± 2.3° for the laser-ablated sample, indicating enhanced hydrophilicity achieved through laser texturing. Post-processing treatments further altered the wettability: heat-treated samples exhibited a Ɵ of 91.8 ± 2.3°, while chemically coated samples showed a Ɵ of 124.8 ± 2.1°. These results demonstrate a transition in wettability toward a hydrophobic state, with HTMS coating being the more effective treatment for achieving this shift. Further, morphology analysis revealed randomly oriented thread-like micro/nanostructures, with coarsening of features in heat-treated samples. This study confirms that laser ablation effectively creates microscale surface features to enhance wettability, while post-processing enables a controlled transition from hydrophilic to hydrophobic states. This tuneable wettability offers promising applications for multi-functional surfaces and heat transfer systems
Palladium-ion-exchanged geopolymer catalyst derived from natural kaolin: an ecofriendly and sustainable catalyst for solventless synthesis of quinoline
No full textThe current study addresses the usage of the natural mineral kaolin as a precursor for synthesizing a geopolymer (GNK)-based new catalytic material. The catalyst was synthesized by introducing palladium ions to the geopolymer matrix via an ion-exchange process. It was found that the obtained palladium-ion-exchanged geopolymer (Pd-GNK) was highly efficient for the synthesis of quinoline using cinnamyl alcohol and aniline as reactants under solventless conditions. Both the geopolymer and catalyst have been thoroughly characterized using various techniques, including CP-MAS NMR, FT-IR, XRD, HR-TEM, FE-SEM, EDX, XPS, ICP-OES, and XRF and BET analysis. Remarkably, under optimal reaction at 150 °C in the presence of a Pd-GNK catalyst, a one-pot quinoline synthesis resulted in a good isolated yield without the use of any solvent. Additionally, the synthesized catalyst demonstrated that it exhibits stability, as evidenced by its sustained catalytic activity across four consecutive cycles. This study highlights the development of a novel catalyst derived from low-cost material and having no carbon footprint contribution during the synthesis. This material has been reported for the first time for quinoline synthesis. The important features of the process are easy workup, simple catalyst recovery, and reusability. This approach is sustainable, ensuring economic feasibility and minimizing environmental impact. These materials present novel opportunities for advancement in the field of catalysis
On the complexity of problems on graphs defined on groups
No full textWe study the complexity of graph problems on graphs defined on groups, especially power graphs. We observe that an isomorphism invariant problem, such as Hamiltonian Path, Partition into Cliques, Feedback Vertex Set, Subgraph Isomorphism, cannot be NP-complete for power graphs, commuting graphs, enhanced power graphs, directed power graphs, and bounded-degree Cayley graphs, assuming the Exponential Time Hypothesis (ETH). An analogous result holds for isomorphism invariant group problems: no such problem can be NP-complete unless ETH is false. We show that the Weighted Max-Cut problem is NP-complete in power graphs. We also show that, unless ETH is false, the Graph Motif problem cannot be solved in quasipolynomial time on power graphs, even for power graphs of cyclic groups. We study the recognition problem of power graphs when the adjacency matrix or list is given as input and show that for abelian groups and some classes of nilpotent groups, it is solvable in polynomial time
Annexin-derived self-assembling peptide nanostructures for alleviation of calcium oxalate -induced renal injury
No full textThe formation of polycrystalline aggregates in the glomerulus or other components of the urinary system is indisputably the most critical step in the formation of kidney stones and calcium oxalate monohydrate (CaC2O4·H2O) is the most prevalent form. On the other hand, Annexin A1 (ANXA1), a calcium-binding protein, markedly increased on the apical surface of renal cells in CaC2O4-induced nephrolithiasis. In this regard, we identified the peptide motif responsible for calcium binding and redesigned it into a self-assembling peptide sequence without disturbing its binding selectivity for the CaC2O4 interface. We developed a salt-dependent strategy to produce self-assembling spherical peptide nanoparticles by using aqueous solutions of R8 peptide and 16-amino acid designed peptide of net charge of -3 (WAEEFLKWLAFIEEFF). Peptide nanoparticles restored cell viability and reduced oxidative stress in MDCK cells triggered by CaC2O4 crystals (80 µg cm− 2) via Nrf2-HO-1 pathway activation. Peptide nanoparticles led to significant protection in urinary biochemistry and reducing calcifications without any toxicity
Search for Gravitational Waves Emitted from SN2023ixf
No full textWe present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz, where we assume the gravitational-wave emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy 1 × 10−4 M⊙c2 and luminosity 2.6 × 10−4 M⊙c2 s−1 for a source emitting at 82 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as 1.08, at frequencies above 1200 Hz, surpassing past results