Real System Research Articles

Multiparameter critical quantum metrology with impurity probes

Quantum systems can be used as probes in the context of metrology for enhanced parameter estimation. In particular, the delicacy of critical systems to perturbations can make them ideal sensors. Arguably the simplest realistic probe system is a spin- 12 impurity, which can be manipulated and measured in-situ when embedded in a fermionic environment. Although entanglement between a single impurity probe and its environment produces nontrivial many-body effects, criticality cannot be leveraged for sensing. Here we introduce instead the two-impurity Kondo model as a novel paradigm for critical quantum metrology, and examine the multiparameter estimation scenario at finite temperature. We explore the full metrological phase diagram numerically and obtain exact analytic results near criticality. Enhanced sensitivity to the inter-impurity coupling driving a second-order phase transition is evidenced by diverging quantum Fisher information (QFI) and quantum signal-to-noise ratio (QSNR). However, with uncertainty in both coupling strength and temperature, the multiparameter QFI matrix becomes singular—even though the parameters to be estimated are independent—resulting in vanishing QSNRs. We demonstrate that by applying a known control field, the singularity can be removed and measurement sensitivity restored. For general systems, we show that the degradation in the QSNR due to uncertainties in another parameter is controlled by the degree of correlation between the unknown parameters.

Efficient river hydrodynamics modelling in realistic river systems using a Fourier neural operator-based network

Effective river management relies heavily on the accurate simulation of river flow dynamics to develop scenario-based strategies and inform decision making. Deep learning (DL) models, as alternatives to conventional simulation techniques, offer a compelling blend of precision and efficiency. This study introduces FNOCL (Fourier Neural Operator with ConvLSTM units), a novel DL neural network for modelling unsteady natural river flows. The network is adept at handling the complexities of spatial and temporal variations. FNOCL builds upon the Fourier neural operator (FNO) by incorporating convolutional long short-term memory (ConvLSTM) layers, combining robust performance with high proficiency in terms of capturing spatiotemporal patterns. Notably, FNOCL accommodates complex river cross-section profiles, thus enabling the accurate modelling of flow dynamics within intricate river morphologies. Our study applies FNOCL to two distinct river networks with varying morphology complexity levels. With reference to the HEC-RAS model, FNOCL showcases its ability to attain high accuracy in model prediction tasks and superior generalization with regard to addressing the uncertainty associated with inflow variations and cross-sectional changes. In comparative analyses with benchmark models (i.e., FNO and ConvLSTM), FNOCL performs most efficiently during the training process and obtains the most accurate water level and river flow predictions in the spatiotemporal domain. Furthermore, FNOCL exhibits a substantial speedup over HEC-RAS, notably reaching up to 50 times in a complex river network. These results highlight FNOCL as an efficient surrogate for conventional simulators, facilitating precise analyses across various scenarios. It provides efficient computations for system responses within complex river flow networks, thereby enhancing applications in river management, such as flood risk assessment, flood control, and the planning and design of hydraulic control structures.

Exploring the Mechanisms behind Non-aromatic Fluorescence with the Density Functional Tight Binding Method.

Recent experimental findings reveal nonconventional fluorescence emission in biological systems devoid of conjugated bonds or aromatic compounds, termed non-aromatic fluorescence (NAF). This phenomenon is exclusive to aggregated or solid states and remains absent in monomeric solutions. Previous studies focused on small model systems in vacuum show that the carbonyl stretching mode along with strong interaction of short hydrogen bonds (SHBs) remains the primary vibrational mode explaining NAF in these systems. In order to simulate larger model systems taking into account the effects of the surrounding environment, in this work we propose using the density functional tight-binding (DFTB) method in combination with non-adiabatic molecular dynamics (NAMD) and the mixed quantum/molecular mechanics (QM/MM) approach. We investigate the mechanism behind NAF in the crystal structure of l-pyroglutamine-ammonium, comparing it with the related nonfluorescent amino acid l-glutamine. Our results extend our previous findings to more realistic systems, demonstrating the efficiency and robustness of the proposed DFTB method in the context of NAMD in biological systems. Furthermore, due to its inherent low computational cost, this method allows for a better sampling of the nonradiative events at the conical intersection which is crucial for a complete understanding of this phenomenon. Beyond contributing to the ongoing exploration of NAF, this work paves the way for future application of this method in more complex biological systems such as amyloid aggregates, biomaterials, and non-aromatic proteins.

Chemical stability of anion exchange membranes for alkaline fuel cells and water electrolysers: Experiment, multi-dimensional modeling and hybrid simulations

The unstable nature of anion exchange membranes (AEMs) has hindered the progress of alkaline fuel cells and water electrolyzers. Density functional theory (DFT) can explore the correlation between the structure and properties of model compounds. However, theoretical explorations of AEMs remain immensely insufficient owing to the complexity of modeling the realistic solution system which caused by multi-dimensional parameters such as polymer chain entanglement, steric hindrance, and migration of ions. In this study, molecular dynamic simulation was utilized to obtain the multi-dimensional parameters of polyelectrolytes, which were considered when calculating the degradation energy barrier by DFT, thus leading to more accurate results and optimizing the conventional DFT calculations. Combined with the computational works, pyridinium functionalized polyolefin and polyether sulfone were synthesized to verify the feasibility of the simulation scheme. This optimized scheme fills the gap in the DFT calculations scale and is easy to extend to other types of AEMs, which help design the chemically stable AEMs and promote the development of alkaline fuel cells and water electrolysers.

Distinct Shortest Walk Enumeration for RPQs

We consider the Distinct Shortest Walks problem. Given two vertices s and t of a graph database D and a regular path query, we want to enumerate all walks of minimal length from s to t that carry a label that conforms to the query. Usual theoretical solutions turn out to be inefficient when applied to graph models that are closer to real-life systems, in particular because edges may carry multiple labels. Indeed, known algorithms may repeat the same answer exponentially many times. We propose an efficient algorithm for graph databases with multiple labels. The preprocessing runs in O(DxA) and the delay between two consecutive outputs is in O(λxA), where A is a nondeterministic automaton representing the query and L is the minimal length. The algorithm can handle epsilon-transitions in A or queries given as regular expressions at no additional cost.

Beyond isotropic repulsion: Classical anisotropic repulsion by inclusion of p orbitals.

Accurate modeling of intermolecular repulsion is an integral component in force field development. Although repulsion can be explicitly calculated by applying the Pauli exclusion principle, this approach is computationally viable only for systems of limited sizes. Instead, it has previously been shown that repulsion can be reformulated in a "classical" picture: the Pauli exclusion principle prohibits electrons from occupying the same state, leading to a depletion of electronic charge between atoms, giving rise to an enhanced nuclear-nuclear electrostatic repulsion. This classical picture is called the isotropic S2/R approximation, where S is the overlap and R is the interatomic distance. This approximation accurately captures the repulsion of isotropic atoms such as noble gas dimers; however, a key deficiency is that it fails to capture the angular dependence of the repulsion of anisotropic molecules. To include directionality, the wave function must at least be a linear combination of s and p orbitals. We derive a new anisotropic S2/R repulsion model through the inclusion of the anisotropic p orbital term in the total wave function. Because repulsion is pairwise and decays rapidly, it can be truncated at a short range, making it amenable for efficient calculation of energy and forces in complex biomolecular systems. We present a parameterization of the S101 dimer database against the abinitio benchmark symmetry-adapted perturbation theory, which yields an rms error of only 0.9 kcal/mol. The importance of the anisotropic term is demonstrated through angular scans of water-water dimers and dimers involving halobenzene. Simulation of liquid water shows that the model can be computed efficiently for realistic system sizes.

Calculating the acoustic radiation force on spherical particles in a standing ultrasound wave field considering single and multiple scattering

Ultrasound directed self-assembly (DSA) utilizes the acoustic radiation force (ARF) associated with a standing ultrasound wave to organize particles dispersed in a fluid medium into specific patterns. The ARF is a superposition of the primary acoustic radiation force, which results from the incident standing ultrasound wave, and the acoustic interaction force, which originates from single and multiple scattering between neighboring particles. In contrast with most reports in the literature that neglect multiple scattering when calculating the ARF, we demonstrate that the deviation between considering single or multiple scattering may reach up to 100%, depending on the ultrasound DSA process parameters and material properties. We evaluate a theoretical case with three spherical particles in a viscous medium and derive operating maps that quantify the deviation between both scattering approaches as a function of the ultrasound DSA process parameters. Then, we study a realistic system with hundreds of particles dispersed in a viscous medium, and show that the deviation between the ARF resulting from single and multiple scattering increases with decreasing particle size and increasing medium viscosity, density ratio, compressibility ratio, and particle volume fraction. This work provides a quantitative basis for determining whether to consider single or multiple scattering in ultrasound DSA simulations.

CyberGrid: an IEC61850 protocol-based substation automation virtual cyber range for cybersecurity research in the smart grid

ABSTRACT A cyber range provides a controlled environment for simulating mission-critical systems for cybersecurity research following the high risks involved in conducting experiments in real-life systems. Existing cyber ranges do not provide close emulation of Cyber-Physical Systems (CPS) in the Smart Grid Networks (SGNs), with emphasis on industry-based proprietary and IED protocols. This paper presents CyberGrid Testbed – a virtual Cyber-Range developed by the CyberSCADA Research Laboratory at Obafemi Awolowo University, Ile-Ife, Nigeria, which emulates Substation Automation processes with IEC61850 and Modbus/TCP protocols and support for MMS, and IED. This was built on OpenPLC6185, simulating Twelve attack patterns, and four attack types.

Sliding dynamics of a Filippov ecological system with nonlinear threshold control and pest resistance

By applying the integrated pest management strategy to address pest control problems, Filippov pest-natural enemy systems have been extensively studied. However, little attention has been paid to pest changing rate and pest resistance in these studies. Therefore, this paper proposes a complex and more realistic Filippov pest-natural enemy system with curved discontinuity boundary by considering Beddington–DeAngelis functional response and pest resistance, as well as incorporating pest changing rate in the threshold control index. Based on the relevant theory of the Filippov system, we analyze a sextic equation and obtain that the proposed Filippov system can have at most six sliding segments. By using qualitative techniques, we discuss the sliding segment and pseudo-equilibrium bifurcations. Numerically, we investigate the local and global sliding bifurcations. It can be detected that one new local sliding bifurcation occurs, which could be called the pseudo-Hopf bifurcation but differs from the traditional pseudo-Hopf bifurcation as it involves two sliding segments with opposite stabilities and generates a sliding limit cycle. We also discover two interesting global sliding bifurcations with novel bifurcation processes, which may be termed triple limit cycle bifurcations as they both relate to the appearance of three nested limit cycles.

New information measures for linear Diophantine fuzzy sets and their applications with LDF-ARAS on data storage system selection problem

In addition to membership and non-membership degrees, linear Diophantine fuzzy set (LDFS) incorporates two reference parameters in fuzzy set definition to represent human judgment more comprehensively. It also provides flexibility in updating the decision setting by changing the meaning of the reference parameters when required by the decision-maker. Information measures in fuzzy sets have many application areas in the literature. Especially, distance and entropy measures are utilized in multiple attribute decision-making (MADM) field. Distance measures are needed by distance-based methods like TOPSIS and EDAS because their alternative ranking procedure is based on measuring the distances between alternatives and some artificial ideal or average solutions. Entropy measure is a very important tool being used in objective attribute weighting procedures of MADM applications. Thus, new distance and entropy measure propositions for LDFS are the first contribution of the study. Also, Additive Ratio Assessment (ARAS) method is extended into LDFS (LDF-ARAS) for the first time in the literature as a second contribution. In this extension, we selected ARAS as one of the most cited tools because it is found flexible and easily adaptable by researchers who do not have any background in MADM. The proposed entropy measure is integrated with the objective attribute weighting procedure of the novel LDF-ARAS approach. The third contribution is the newly developed decision model proposed for a real-life data storage system (DSS) selection problem. Three DSS options were evaluated with respect to thirteen attributes by five skilled and well-educated experts and LDF-ARAS was performed to rank alternatives. The entropy-based LDF-ARAS approach was validated by comparing the results of six different applications. In the first three, different entropy formulas are used and in the last three, these entropy measures are integrated with LDF-TOPSIS. All six comparisons gave similar alternative rankings.

Visual social information use in collective foraging.

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness (TTD) and total energy consumption (TEC) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize TEC. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

Elevated CO2 exacerbates the risk of methylmercury exposure in consuming aquatic products: Evidence from a complex paddy wetland ecosystem

Elevated CO2 levels and methylmercury (MeHg) pollution are important environmental issues faced across the globe. However, the impact of elevated CO2 on MeHg production and its biological utilization remains to be fully understood, particularly in realistic complex systems with biotic interactions. Here, a complete paddy wetland microcosm, namely, the rice-fish-snail co-culture system, was constructed to investigate the impacts of elevated CO2 (600 ppm) on MeHg formation, bioaccumulation, and possible health risks, in multiple environmental and biological media. The results revealed that elevated CO2 significantly increased MeHg concentrations in the overlying water, periphyton, snails and fish, by 135.5%, 66.9%, 45.5%, and 52.1%, respectively. A high MeHg concentration in periphyton, the main diet of snails and fish, was the key factor influencing the enhanced MeHg in aquatic products. Furthermore, elevated CO2 alleviated the carbon limitation in the overlying water and proliferated green algae, with subsequent changes in physico-chemical properties and nutrient concentrations in the overlying water. More algal-derived organic matter promoted an enriched abundance of Archaea-hgcA and Deltaproteobacteria-hgcA genes. This consequently increased the MeHg in the overlying water and food chain. However, MeHg concentrations in rice and soil did not increase under elevated CO2, nor did hgcA gene abundance in soil. The results reveal that elevated CO2 exacerbated the risk of MeHg intake from aquatic products in paddy wetland, indicating an intensified MeHg threat under future elevated CO2 levels.

Simultaneously Calibration of Multi Hand–Eye Robot System Based on Graph

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. Systems with multi-eyes (cameras) and multi-hands (robots) are particularly sensitive to calibration errors. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the pose between each unit, but the serialized incremental calibration strategy cannot avoid the error accumulation problem in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization with different sensor's accuracy considered. As a general calibration method, it can be applied to different multi-robot systems. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method.

Predictive analysis of power degradation rate in solar PV systems emphasizing hot spots and visual effects-based failure modes

A definite and exclusive long-term PV degradation projection is required to reduce financial vulnerability in the solar photovoltaic plant-based energy transition markets. So, in this paper beside a traditional linear rate approach, a non-linear design tool for quantifying long-term failure mode-based power degradation rate (Rd), is proposed. An on-field experimental investigation incorporating visual effects-based Solar I–V characterization and thermal imaging for a duration of 1 year (February 2022 to January 2023) is carried out on a realistic solar PV system exposed to field conditions for 5–6 years. The experimentally evaluated Rd is 7.7 %/year. Two non-linear failure mode-based Rd models employing machine learning approaches, with differences in inputs are developed and are proven economically useable against the reported assessment techniques. The root mean square error (RMSE) for model 1 and model 2 during testing is 0.0019 and 0.0020 respectively (LSTM based approach) whereas testing error for model 1 and model 2 using back propagation based approach is 0.00182 and 0.00132 respectively. The proposed tool is also validated for a realistic PV plant in Telangana. A closeness of prediction against the actual of 9.0 % is observed for model 1 during validation, whereas the same for model 2 is 9.6 %.

Beyond Born-Oppenheimer Treatment for Multi-State Photoelectron Spectra, Phase Transitions of Solids and Scattering Processes

The “completeness” and “accuracy” of first principle based Beyond Born-Oppenheimer (BBO) theory is reviewed with its theoretical developments and wide applications on various realistic spectroscopic systems, scattering processes and pervoskite molecules exhibiting phase transition phenomena. Over the last two decades, our group has formulated as well as implemented the BBO treatment to construct diabatic Hamiltonian for several spectroscopically interesting molecules (NO2 radical, Na3 and K3 clusters, NO3, C6H6+,1,3,5-C6H3F3+ and C4N2H4), octahedral units of perovskites ( MnO69− and TiO68− ) involving solid state phenomenon as well as triatomic reactive scattering processes ( H3+ , HeH2+ and F+H2) to depict the effects of electron-nuclear couplings. Such diabatic Hamiltonians have been further used to perform quantum dynamical calculations to obtain observables (photoelectron spectra for spectroscopic/solid state systems and cross-sections/rate constants for scattering processes), which show excellent agreement with the recent experimental findings. In summary, this article provides the current perspective of BBO approach as well as Jahn-Teller (JT) theory with various applications on molecular systems/chemical processes.

Analysis of the Difference Between PUBG and FORTNITE

PUBG is a video game that was created in South Korea. Following its release, the book gained significant popularity in East Asia and was subsequently acquired by China's Tencent Game Company. Subsequently, it underwent revisions and transformed into the Chinese iteration of PUBG, renowned as "Peace Elite". FORTNITE originated in North Carolina, USA, and has gained immense popularity across the United States. The purpose of this study is to conduct a comparative analysis of 100-player competitive games in East Asia and the United States. The study will utilize game interaction comparison, game design comparison, and game experience comparison to identify the strengths and weaknesses of each region. The study's findings indicate that PUBG's earlier publication has resulted in a more thorough and realistic system and game settings. The cartoonish aesthetics and futuristic elements of FORTNITE have garnered significant appeal among a large number of young players.

Applications of 3D Bioprinting Technology to Brain Cells and Brain Tumor Models: Special Emphasis to Glioblastoma.

Primary brain tumor is one of the most fatal diseases. The most malignant type among them, glioblastoma (GBM), has low survival rates. Standard treatments reduce the life quality of patients due to serious side effects. Tumor aggressiveness and the unique structure of the brain render the removal of tumors and the development of new therapies challenging. To elucidate the characteristics of brain tumors and examine their response to drugs, realistic systems that mimic the tumor environment and cellular crosstalk are desperately needed. In the past decade, 3D GBM models have been presented as excellent platforms as they allowed the investigation of the phenotypes of GBM and testing innovative therapeutic strategies. In that scope, 3D bioprinting technology offers utilities such as fabricating realistic 3D bioprinted structures in a layer-by-layer manner and precisely controlled deposition of materials and cells, and they can be integrated with other technologies like the microfluidics approach. This Review covers studies that investigated 3D bioprinted brain tumor models, especially GBM using 3D bioprinting techniques and essential parameters that affect the result and quality of the study like frequently used cells, the type and physical characteristics of hydrogel, bioprinting conditions, cross-linking methods, and characterization techniques.

Cost minimization of a hybrid PV-to-methanol plant through participation in reserve markets: A Danish case study

In efforts to combat carbon emissions in challenging sectors like heavy industry and shipping, Power-to-X (PtX) technology emerges as a promising solution. PtX involves converting renewable energy into hydrogen via water electrolysis and further processing it into eco-friendly fuels like methanol, ammonia, or kerosene. Denmark has seen a rise in plans for integrating large-scale PtX projects, which will introduce large electrical loads to its power system.To optimize the cost-effectiveness of PtX-based fuel production, a proposal suggests leveraging PtX plants’ inherent flexibility to provide frequency reserves to the electrical grid. The study centers on a real-life hybrid renewable energy system (HRES) dedicated to methanol production in Denmark.Analyzing the HRES technology and current reserve markets reveals that the HRES qualifies for frequency reserve provision certification, encompassing FCR, aFRR, and mFRR, by the transmission system operator. Through participation in the reserve market, the HRES can generate added revenue, resulting in substantial reductions of 12% and 30% in the levelised cost of methanol for 2020 and 2021 operational expenditure outcomes, respectively.The study illustrates significant cost reductions achievable through engagement in reserve markets, and these reductions may escalate further with increased market prices and volatility. Participating in frequency reserve capacity markets not only facilitates overall cost reduction but also serves as an insurance policy against unpredictable energy market fluctuations. This approach presents a promising trajectory to promote PtX technology adoption and nurture sustainable energy practices in challenging sectors.

The Effect of Spin Relaxation on Magnetic Compass Sensitivity in ErCry4a.

This study explores the impact of thermal motion on the magnetic compass mechanism in migratory birds, focusing on the radical pair mechanism within cryptochrome photoreceptors. The coherence of radical pairs, crucial for magnetic field inference, is curbed by spin relaxation induced by intra-protein motion. Molecular dynamics simulations, density-functional-theory-based calculations, and spin dynamics calculations were employed, utilizing Bloch-Redfield-Wangsness (BRW) relaxation theory, to investigate compass sensitivity. Previous research hypothesized that European robin's cryptochrome 4a (ErCry4a) optimized intra-protein motion to minimize spin relaxation, enhancing magnetic sensing compared to the plant Arabidopsis thaliana's cryptochrome 1 (AtCry1). Different correlation times of the nuclear hyperfine coupling constants in AtCry1 and ErCry4a were indeed found, leading to distinct radical pair yields in the two species, with ErCry4a showing optimized sensitivity. However, this optimization is likely negligible in realistic spin systems with numerous nuclear spins. Beyond insights in magnetic sensing, the study presents a detailed method employing molecular dynamics simulations to assess spin relaxation effects on chemical reactions with realistically modelled protein motion, relevant for studying radical pair reactions at finite temperature.