Search Results (2,693)

The paper systematically investigated the flotation behavior and interaction mechanisms of andalusite and quartz under sodium dodecyl sulfonate (SDS) through integrated experimental and computational approaches, including zeta potential measurements, Fourier-transform infrared (FTIR) spectroscopy, Materials Studio (MS)-based quantum chemical calculations, and single-mineral flotation tests. The results of zeta potential and infrared spectroscopy analysis indicated that SDS underwent strong chemical adsorption on the surface of andalusite, while the adsorption effect on the surface of quartz was not obvious. MS calculations showed that the {100} surface energy of andalusite was the lowest, and it was the most important dissociation surface. After SDS was adsorbed on the {100} surface of andalusite, the aluminum atoms on the surface of andalusite lost electrons, resulting in a significant increase in the number of positive charges they carried. The activity of oxygen atoms was enhanced, while the number of charges carried by silicon atoms changed relatively little. It was indicated that SDS adsorbed the active sites of Al atoms on the surface of andalusite. The results of the pure mineral flotation test further verified the accuracy of the previous test results, indicating that andalusite and quartz had a good flotation separation effect under the SDS system. Full article

Electro-optic modulators in the near-infrared spectrum are finding applications in atomic clocks, quantum sensing, quantum information processing, and high-precision measurement. We developed thin-film lithium niobate electro-optic modulators operating at 795 nm for modulation around the D₁ line of ⁸⁷Rb with satisfactory overall performance. Specifically, we made a systematic improvement to reduce the insertion loss, including widening the modulation waveguides, thickening the overcladding, polishing and coating the facets. The fabricated device possesses a low insertion loss of 7.6 dB, an extinction ratio exceeding 30 dB, a 3 dB modulation bandwidth of ~22 GHz, a half-wave voltage-length product of ~1.8 Vcm, and strong adaptability for packaging. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience. Full article

This manuscript is intended for readers who have a general interest in the subject of quantum computation and provides an overview of the most significant developments in the field. It begins by introducing foundational concepts from quantum mechanics—such as superposition, entanglement, and the no-cloning theorem—that underpin quantum computation. The primary computational models are discussed, including gate-based (circuit) quantum computing, adiabatic quantum computing, measurement-based quantum computing and the quantum Turing machine. A selection of significant quantum algorithms are reviewed, notably Grover’s search algorithm, Shor’s factoring algorithm, and Quantum Singular Value Transformation (QSVT), which enables efficient solutions to linear algebra problems on quantum devices. To assess practical performance, we compare quantum and classical implementations of support vector machines (SVMs) using several synthetic datasets. These experiments offer insight into the capabilities and limitations of near-term quantum classifiers relative to classical counterparts. Finally, we review leading quantum programming platforms—including Qiskit, PennyLane, and Cirq—and discuss their roles in bridging theoretical models with real-world quantum hardware. The paper aims to provide a concise yet comprehensive guide for those looking to understand both the theoretical foundations and applied aspects of quantum computing. Full article

The nitrogen-vacancy (NV) center magnetic sensor, leveraging nitrogen-vacancy quantum effects, enables high-sensitivity magnetic field detection via optically detected magnetic resonance (ODMR). However, conventional single-point integrated devices suffer from limitations such as inefficient regional magnetic field detection and challenges in discerning the directional variations of dynamic magnetic fields. To address these issues, this study proposes an array- based architecture that innovatively substitutes the conventional 532 nm laser with quantum-dot light-emitting diodes (QLEDs). Capitalizing on the advantages of QLEDs—including compatibility with micro/nano-fabrication processes, wavelength tunability, and high luminance—a 2 × 2 monolithically integrated magnetometer array was developed. Each sensor unit achieves a magnetic sensitivity of below 26 nT·Hz^−1/2 and a measurable range of ±120 μT within the 1–10 Hz effective bandwidth. Experimental validation confirms the array’s ability to simultaneously resolve multi-regional magnetic fields and track dynamic field orientations while maintaining exceptional device uniformity. This advancement establishes a scalable framework for the design of large-scale magnetic sensing arrays, demonstrating significant potential for applications requiring spatially resolved and directionally sensitive magnetometry. Full article

Parameterized quantum circuits play a key role in quantum computing. Measuring the suitability of such a circuit for solving a class of problems is needed. One such promising measure is the expressivity of a circuit, which is defined in two main variants. The variant in focus of this contribution is the so-called dimensional expressivity, which measures the dimension of the submanifold of states produced by the circuit. Understanding this measure needs a lot of background from differential topology, which makes it hard to comprehend. In this article, we provide this background in a vivid as well as pedagogical manner. Especially, it strives towards being self-contained for understanding expressivity, e.g., the required mathematical foundations are provided, and examples are given. Also, the literature makes several statements about expressivity, the proofs of which are omitted or only indicated. In this article, we give proof for key statements from dimensional expressivity, sometimes revealing limits for generalizing them, and sketching how to proceed in practice to determine this measure. Full article

Understanding how environmental factors regulate photosynthetic energy partitioning is crucial for enhancing crop resilience in future climates. This study investigated the light-response dynamics of sweet sorghum (Sorghum bicolor L. Moench) leaves under combinations of CO₂ concentrations (250, 410, and 550 μmol mol⁻¹) and temperatures (30 °C and 35 °C), using integrated chlorophyll fluorescence measurements and mechanistic photosynthesis modeling. Our results revealed that elevating CO₂ from 250 to 550 μmol mol⁻¹ significantly increased the maximum electron transport rate (J_max) by up to 57%, and enhanced the effective light absorption cross-section (σ′_ik) by 64% under high light and elevated temperature (35 °C), indicating improved photochemical efficiency and light-harvesting capability. Concurrently, these adjustments reduced PSII down-regulation. Increased temperature stimulated thermal dissipation, reflected in a rise in non-photochemical quenching (NPQ) by 0.13–0.26 units, accompanied by a reduction in the number of excited-state pigment molecules (N_k) by 20–33%. The strongly coordinated responses between quantum yield (Φ_PSII) and σ′_ik highlight a dynamic balance among photochemistry, heat dissipation, and fluorescence. These findings elucidate the synergistic photoprotective and energy-partitioning strategies that sweet sorghum employs under combined CO₂ enrichment and heat stress, providing mechanistic insights for optimizing photosynthetic performance in C₄ crops in a changing climate. Full article

Sol–gel-processed zinc oxide (ZnO) and magnesium-doped zinc oxide (ZnMgO) are widely used in quantum dot light-emitting diodes (QLEDs) due to their excellent charge transport properties, ease of fabrication, and tunable film characteristics. In particular, the ZnMgO/ZnO bilayer structure has attracted considerable attention for its dual functionality: defect passivation by ZnMgO and efficient charge transport by ZnO. However, while the effects of resistive switching (RS) in individual ZnO and ZnMgO layers on the aging behavior of QLEDs have been studied, the RS characteristics of sol–gel-processed ZnMgO/ZnO bilayers remain largely unexplored. In this study, we systematically analyzed RS properties of an indium tin oxide (ITO)/ZnMgO/ZnO/aluminum (Al) device, demonstrating superior performance compared to devices with single layers of either ZnMgO or ZnO. We also investigated the shelf-aging characteristics of RS devices with single and bilayer structures, finding that the bilayer structure exhibited the least variation over time, thereby confirming its enhanced uniformity and reliability. Furthermore, based on basic current–voltage measurements, we estimated accuracy variations in MNIST pattern recognition using a two-layer perceptron model. These results not only identify a promising RS device architecture based on the sol–gel process but also offer valuable insights into the aging behavior of QLEDs incorporating ZnMgO/ZnO bilayers, ITO, and Al electrodes. Full article

In this study, we aimed to evaluate physiological and agronomic traits in 120 sugarcane genotypes under early drought stress conditions in a field trial across various soil types. The experiment used a split-plot arrangement, with a randomized complete block design and two replications. Two different water regimes were assigned to the main plot: (1) non-water stress (CT) and (2) drought (DT) at the early growth stage, during which sugarcane was subjected to drought stress by withholding water for 4 months. The subplot consisted of 120 sugarcane genotypes. The stalk height, stalk diameter, number of stalks, photosynthetic traits including SPAD chlorophyll meter reading (SCMR) and maximum quantum efficiency of photosystem II photochemistry (Fv/Fm), and normalized difference vegetation index (NDVI) were measured at 3, 6, and 9 months after planting (MAP). Yield and yield component parameters were measured at 12 MAP. Drought treatments lead to significant changes in various physiological traits in the sugarcane. Clustering analysis classified 36 sugarcane varieties grown in sandy loam soil and 15 genotypes in loam soil into two main clusters. In sandy loam soils, Biotec4 and CO1287 exhibited outstanding performance in drought conditions, delivering high cane yields. Meanwhile, in loam soil, MPT13-118, MPT07-1, Q47, F174, MPT14-1-902, and UT1 exhibited the best drought tolerance. Under drought conditions, cluster 1 showed higher values for SCMR, NDVI, height growth rate (HGR), cane yield, and drought tolerance index compared to cluster 2. These findings suggest that breeders can utilize these genotypes to enhance drought resistance, and the identified physiological traits can assist in selecting stronger candidates for drought tolerance. Full article

Our research goal is to provide the mathematical guidance to enable any combination of “intelligent” machines, artificial intelligence (AI) and humans to be able to interact with each other in roles that form the structure of a team interdependently performing a team’s tasks. Our quantum-like model, representing one of the few, if only, mathematical models of interdependence, captures the tradeoffs in energy expenditures a team chooses as it consumes its available energy on its structure versus its performance, measured by the uncertainty (entropy) relationship generated. Here, we outline the support for our quantum-like model of uncertainty relations, our goals in this study, and our future plans: (i) Redundancy reduces interdependence. This first finding confirms the existence of interdependence in systems, both large and small. (ii) Teams with orthogonal roles perform best. This second finding is the root cause of humans, including scientists, being unable to appreciate the role of interdependence in “squeezing” states of teams. (iii) Cognitive reports may not equal behavior. The last finding allows us to tie our research together and to account for the absence of social scientists from leading the mathematical science of teams. In this article, we review the need for a mathematics for the future of team operations, the literature, the mathematics in our model of agents with full agency (viz., intelligent and interdependent), our hypothesis that freely organized teams enjoy significant advantages over command decision-making (CDM) systems, and results from the field. We close with future plans and a generalization about squeezing states to control interdependent systems. Full article

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

Ru₃Sn₇ crystallizes in the cubic Ir₃Ge₇-type structure (space group Im3m), a class of intermetallic compounds. Previous studies focused primarily on its crystal structure, band calculations, and basic transport properties. Here, we report a systematic investigation of high-quality single crystals via electrical resistivity, Hall effect, specific heat, and thermal transport measurements. The T₃X₇ intermetallic family—with its diverse electronic ground states—provides an ideal platform for exploring such topology–property relationships. Ru₃Sn₇ exhibits metallic behavior, with consistent Hall effect and Seebeck coefficient data indicating a compensated electron-hole two-band system. Temperature-dependent modulation of electronic states near the Fermi surface alters charge carrier transport, which may imply the presence of a Lifshitz transition in Ru₃Sn₇. More importantly, magnetic quantum oscillations are observed for the first time, confirming the presence of two Dirac points in its band structure. Full article

This study investigated the optimization of optical film configurations to mitigate angular color deviation—a persistent challenge in quantum dot (QD) backlight displays. A white backlight was implemented by placing a yellow CdSe-based QD film on a blue edge-lit backlight, followed by various combinations of prism and diffusion films. Optical characteristics, including luminance, spectral distribution, and chromaticity coordinates, were systematically measured over a viewing-angle range of −70° to 70° for different film arrangements. Applying one or two prism films significantly enhanced normal luminance and improved color conversion efficiency by forming vertical optical cavities; however, this also introduced the side-lobe phenomenon, leading to color non-uniformity. Placing a diffusion film between the QD and prism films did not resolve these issues, whereas positioning it as the topmost layer above the prism films effectively eliminated color dispersion and produced a uniform luminance distribution. These results provide practical design guidelines for optimizing optical film stacks in QD-enhanced backlight units to achieve superior color uniformity in LCD displays. Full article

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions. Full article