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Widefield NV magnetic field reconstruction for probing the Meissner effect and critical current density under pressure
The spatial distribution of a magnetic field can be determined with micrometer resolution using widefield nitrogen vacancy (NV) center magnetic imaging. Nevertheless, reconstructing the magnetic field from the raw data can be challenging due to the degeneracy of the four possible NV axes and the tremendous amount of data. While a qualitative approach is sufficient for most analyses, a quantitative analysis offers deeper insight into the physical system. Here, we apply NV widefield magnetic imaging to a HgBa2Ca2Cu3O8+δ (Hg-1223) superconducting microcrystal at a pressure of 4 GPa. We fit the results with solutions from the Hamiltonian describing the NV center ground state and take into account the relative intensities of the resonances to determine the local magnetic field magnitude and angle. Thus, we reconstruct the temperature-dependent expulsion of the magnetic field associated with the Meissner effect around the superconductor. By comparing the resulting parameters to Brandt's model, which describes the magnetic behavior of a type-II superconductor, we extract the critical current density jc. Overall, this work showcases the first widefield quantitative reconstruction of the field screening under pressure and an optical method to study critical current density. Thus, it provides new insights into the application of NV magnetometry to superconductivity research at high pressures.
Enhancing photovoltaic efficiency in arid climates using cooling strategies
Abstract This study experimentally investigates the performance enhancement of photovoltaic (PV) panels using different cooling techniques under real outdoor operating conditions. Three cooling approaches are evaluated, including water-spray cooling, serpentine water circulation, and a fame-glass configuration. The objective is to assess their impact on PV surface temperature and electrical performance. Experimental measurements are conducted under similar environmental conditions, and the current–voltage (I–V) characteristics of the PV modules were recorded for each cooling configuration. The results demonstrate that active cooling techniques significantly reduced the operating temperature of the PV module, which consequently improved its electrical performance. Among the tested methods, the water-spray cooling technique achieved the most effective temperature reduction and the highest improvement in power output. In contrast, the fame-glass configuration showed a reduction in electrical power despite lowering the panel temperature, mainly due to optical losses and partial shading caused by the glass frame structure. The findings highlight the importance of selecting appropriate cooling strategies that balance thermal management and optical performance. Overall, the results confirm that effective cooling techniques can enhance PV efficiency and contribute to improved energy production in hot climate regions.
Phenome-wide analysis of downstream health outcomes following second-line antidiabetic agent prescriptions in All of Us
Resonant nanoscale magnonic neurons
We use micromagnetic simulations to demonstrate neuron functionality of two-dimensional (2D) chiral magnonic resonators. Our design exploits nonlinear resonant scattering of spin waves propagating in a YIG medium from an edge mode of a permalloy nano-element. The reduced frequency and volume of the edge mode facilitate matching it to the YIG modes and give rise to their wide-angle scattering. As the amplitudes of the incident spin waves increase, the edge mode exhibits a positive nonlinear frequency shift. This shift leads to a complex frequency-dependent nonlinear variation of the amplitude and phase of spin waves scattered in different directions. We show that the scattered waves are strong enough to “activate” secondary neurons. This provides the connectivity required for combining our proposed neurons into 2D magnonic neural networks.
Fault detection in seismic data using a true 3D global attention convolutional network with self-supervised denoising pretext training
SOFisher: reinforcement learning-guided experiment designs for spatial omics
Effective lateral isolation for hydrogen-terminated diamond field-effect transistors via nitrogen ion implantation
Field-effect transistors (FETs) based on hydrogen-terminated (H-terminated) diamond have attracted growing attention for their potential in power electronics and communication technologies. The conventional lateral isolation process in these FETs involves masking the conductive H-terminated regions with polymer resist or metal film, and then converting the unmasked regions into insulating oxygen-terminated diamond through oxygen plasma or other oxidizing treatments. However, this process carries a substantial risk of contaminating the H-terminated diamond surface with residues of the mask and its remover, which potentially degrades FET performance. Herein, we present a fundamentally different approach to lateral isolation in H-terminated diamond FETs through selective nitrogen ion implantation prior to the global H-termination. Electrical measurements confirm that the implanted regions remain highly resistive even after hydrogenation, providing effective and stable lateral isolation. Notably, H-termination is performed after ion implantation, which ensures a clean surface free from residues. This approach also facilitates fabrication of diamond FETs without exposing the hydrogenated surface to air, thus minimizing contamination from airborne impurities. Hence, the proposed lateral isolation technique offers a cleaner and more reliable pathway toward high-performance diamond-based electronic devices.
Cultivated land transformation and new-type urbanization couple from Imbalance to coordination in China’s Fenhe River Basin
Abstract This study examines the complex interdependent relationship between the transformation of cultivated land use and new-type urbanization at the county level within the Fenhe River Basin, highlighting its significant practical implications for the sustainable use of cultivated land and the advancement of high-quality new-type urbanization at the county level in China. Utilizing methodologies such as kernel density estimation, the coupling coordination degree model, and geographic detector methods, this research assesses the comprehensive levels of both cultivated land use transformation and new-type urbanization at the county level. Additionally, it explores the coupling relationship between these two phenomena and identifies their influencing factors. The findings indicate that:(1)There has been a consistent increase in the level of cultivated land use transformation, accompanied by a notable rise in the number of counties reaching high transformation level.(2)The comprehensive level of new-type urbanization at the county level has also shown a steady increase, with the proportion of counties at very high urbanization levels climbing to 43.9%, and these counties exhibit a clustered spatial distribution.(3)The overall level of coupling coordination between cultivated land use transformation and new-type urbanization at the county level has improved, suggesting enhanced coordinated development and reduced contradictions between these two processes. Notably, 90.24% of counties have upgraded their coupling coordination degree to either intermediate or high-levels.(4)The spatial differentiation of coupling coordination levels between cultivated land use transformation and new-type urbanization at the county level in the Fenhe River Basin has evolved from intermediate dissonance to high-level coordination, with this spatial pattern associated with a combination of natural and socio-economic factors. These insights will support the orderly utilization of cultivated land resources and foster the high-quality development of county-level urbanization in the Fenhe River Basin.
Ultra-broadband wireless rectification and frequency mixing via the nonlinear Hall effect in TaIrTe4
Quantum well-modulated transport and voltage-controlled magnetic anisotropy in Cr/Fe/MgO junctions at room temperature
Quantum well (QW) states in ultrathin Fe layers can strongly influence transport and magnetic anisotropy in magnetic tunnel junctions, yet their formation in Fe/MgO systems is complicated by interface roughness and lattice mismatch. We fabricate high-quality single-crystal Cr/Fe/MgO multilayers to enhance QW confinement and study their effect on tunnel magnetoresistance (TMR) and voltage-controlled magnetocrystalline anisotropy (VCMA) at room temperature. Conductance measurements reveal clear resonant peaks, confirming QW formation. The strong QW produces a notable bias shift of the TMR maximum and a non-linear modulation of perpendicular magnetic anisotropy, enabling sign-tunable VCMA. We further examine how an interfacial Ir layer modifies the QW behavior and coupling to transport and anisotropy.
Adaptive deep conditional random field with blockchain for secure data sharing in software-defined wireless body area networks
Abstract Presently, blockchains are widely employed to execute the secure data transmission process among users. However, sharing the sensitive information about the patients among multiple users in the healthcare sector is difficult due to integrity and confidentiality issues. Thus, to tackle these issues in prior models, a secure data-sharing framework for Software-Defined Wireless Body Area Networks (SDWBANs) is designed. In order to ensure privacy and controlled access in SDWBANs, blockchain technology and encryption techniques are considered, which help to protect sensitive medical data. Then, the Adaptive Deep Conditional Random Field (ADCRF) is employed to perform the decision-making procedure. Further, an advanced encryption technique, Optimal Key-based Multi-Authority Attribute-Based Encryption (O-MA-ABE), is employed to ensure that authorized users can access the confidential health data. Here, the Modified Escape Search-based Piranha Foraging Optimization Algorithm (MES-PFOA) is employed to tune the Hyperparameters of ADCRF and keys of O-MA-ABE. Then, the overall performance of the developed framework is compared with classical approaches using metrics such as computational time, decryption time, and decision-making accuracy. In various validations, the developed MES-PFOA-O-MA-ABE + ADCRF-based data sharing model accomplished higher accuracy as 98.7%, precision as 98.3%, minimal encryption time as 210 (ms) and throughput as 275 (TPS) than the recent data sharing models like DTAC-TL-QM, SCCE-DS, BFL-hIoT and PPFL-ICP.
Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trial
Probing the Meissner effect in single crystals of Bi2Sr2Ca2Cu3O10+δ via wide-field quantum microscopy under high pressure
We investigated the pressure dependence of the superconducting transition temperature (Tc) in optimally doped Bi2Sr2Ca2Cu3O10+δ (Bi-2223) single crystals using different pressure-transmitting media. Previous high-pressure studies have reported conflicting behaviors, ranging from a resurgence of Tc of optimally doped Bi-2223 in fluid media to an insulating-like transition in solid media. However, a direct comparison of the effects of different pressure-transmitting media is lacking. Here, we employed wide-field quantum microscopy based on nitrogen-vacancy centers to probe the magnetic response under high pressure, utilizing cBN and KBr as media. We observed that a diamagnetic response near 70 K, indicative of the superconducting transition, persisted up to 23 GPa in KBr, whereas it disappeared above 11 GPa and 70 K in cBN. These results demonstrate the high sensitivity of Bi-2223 to the pressure environment and highlight the critical role of hydrostatic pressure in cuprate superconductors.
Determinants of early postnatal care utilization among mothers in West Shoa Oromia, Ethiopia, an unmatched case-control study 2023
HBV HBx protein masks epigenetic reader Spindlin1 via an inter-molecular zinc finger to subvert transcriptional control
Resource-efficient parallel entanglement generation for multinode quantum networks via time-bin multiplexing
Nonlocal entanglement generation among multiple remote quantum nodes provides a critical foundation for a variety of counterintuitive quantum applications. The exponential loss of photons transmitting over optical fibers sets an upper limit for entangling these quantum nodes. Here, we propose a resource-efficient and parallel protocol for entangling multiple remote quantum nodes via time-bin multiplexing. The transmission of a single photon with qudit-encoding in the time-bin mode enables entangling multiple stationary qubits in parallel when single photons and individual stationary qubit interfaces are used and photon-state modulations are properly introduced before subsequently impinging the photon into each interface. Our protocol can generate parallel multipartite entanglement among (N≥3) quantum nodes with the dimension of the photonic time bins independent of N, exponentially reducing the requirements for the coherence time of the stationary qubits and for the complexity of the photonic modulations. These distinct features make our protocol particularly advantageous for the development of multinode quantum networks.
Ensemble machine learning with Bayesian optimization predicts bioactive extraction from fermented watermelon rind using natural deep eutectic solvents
Abstract A new integrated extraction strategy was established by combining natural solvent systems (NADES), microwave-assisted extraction, and machine learning techniques for optimizing the extraction of bioactive compounds from fermented watermelon rind. The results showed that solid-state fermentation significantly improved extraction rates, compared to non-fermented controls. The fermentation pretreatment comparison was conducted under identical extraction conditions, with non-fermented watermelon rind serving as the internal control. Machine learning models optimized using Bayesian optimization were developed to describe and predict three important response variables: TPC, TFC, and antioxidant activity, as functions of four extraction variables (microwave power, temperature, time, and solid-liquid ratio). The ensemble models showed outstanding predictive capabilities with test R² values of 0.9147, 0.9088, and 0.9252 for TFC, TPC, and DPPH activity, respectively, with very low overfitting (ΔR² < 0.06). Importance analysis of the features showed temperature as the most important parameter in the extraction of bioactive compounds (importance: 0.842–0.885), followed by solid-liquid ratio. Simultaneous optimization using the validated models showed the optimal extraction conditions to be 62.5 °C, 27.7 min, 300 W, and 30 mg/mL, predicting values of 1.656 mg CE/g (TFC), 19.80 mg GAE/g (TPC), and 71.27% (DPPH activity). Solid-state fermentation enhanced extraction yields, increasing total phenolics (16.9 to 19.1 mg GAE/g), flavonoids (0.61 to 0.74 mg CE/g), and DPPH activity (66% to 75%). The developed ensemble models showed high accuracy (R² = 0.91–0.93; RMSE = 0.0812 mg/g, 0.51 mg/g, and 0.74%). Experimental validation results have confirmed the high accuracy of the model with relative errors of less than 3% for all responses. The combination of fermentation pretreatment, green extraction, and machine learning indicates promise for sustainable valorization of agricultural waste resources as sources of bioactive compounds for nutraceutical applications.
De novo design of DNA origami with a generative diffusion model
Adaptive organic phototransistor for light-suppressed regulation
Light-adaptive synapse devices rely on physical mechanisms capable of dynamically responding to light intensity changes, where the negative photoconductivity (NPC) effect is crucial for achieving light-suppressed regulation. This study constructs an adaptive organic phototransistor (AOPT) based on a pentacene/PTCDI-C13 p–n heterojunction, systematically investigating the NPC mechanism and its regulation of photosynaptic plasticity. The device employs PVN and P(VDF-TrFE-CFE) as the capture layer and dielectric layer, respectively. The recombination of photogenerated carriers at the p–n interface, coupled with the capture effect of PVN, jointly induces a significant NPC response. Based on this mechanism, the device achieves synaptic inhibitory plasticity regulation dependent on light pulse intensity, duration, number, and frequency, while exhibiting wavelength selectivity. By integrating electrically driven long-term potentiation with optically driven long-term depression, the device successfully simulates the human eye's light–dark adaptation process. In artificial vision systems, the AOPT array achieved image recognition accuracies of 86.88% and 86.31% under light and dark adaptation conditions, respectively. This study provides a mechanistic foundation and design strategy for photo-electro-optic cooperative plasticity in organic optoelectronic devices, demonstrating their potential for visual information processing under complex illumination conditions.