Scientific Applications Research Articles

Dynamic frequent subgraph mining algorithms over evolving graphs: a survey

Frequent subgraph mining (FSM) is an essential and challenging graph mining task used in several applications of the modern data science. Some of the FSM algorithms have the objective of finding all frequent subgraphs whereas some of the algorithms focus on discovering frequent subgraphs approximately. On the other hand, modern applications employ evolving graphs where the increments are small graphs or stream of nodes and edges. In such cases, FSM task becomes more challenging due to growing data size and complexity of the base algorithms. Recently we see frequent subgraph mining algorithms designed for dynamic graph data. However, there is no comparative review of the dynamic subgraph mining algorithms focusing on the discovery of frequent subgraphs over evolving graph data. This article focuses on the characteristics of dynamic frequent subgraph mining algorithms over evolving graphs. We first introduce and compare dynamic frequent subgraph mining algorithms; trying to highlight their attributes as increment type, graph type, graph representation, internal data structure, algorithmic approach, programming approach, base algorithm and output type. Secondly, we introduce and compare the approximate frequent subgraph mining algorithms for dynamic graphs with additional attributes as their sampling strategy, data in the sample, statistical guarantees on the sample and their main objective. Finally, we highlight research opportunities in this specific domain from our perspective. Overall, we aim to introduce the research area of frequent subgraph mining over evolving graphs with the hope that this can serve as a reference and inspiration for the researchers of the field.

Efficient mapping of phase diagrams with conditional Boltzmann Generators

Abstract The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g., temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed.

A Comprehensive Review of Electromyography in Rehabilitation: Detecting Interrupted Wrist and Hand Movements with a Robotic Arm Approach

Electromyography (EMG) is a diagnostic technique that measures the electrical activity generated by skeletal muscles. Utilizing electrodes placed either on the skin (surface EMG) or inserted directly into the muscle(intramuscular EMG), it detects electrical signals produced during muscle contractions. EMG is widely employed in clinical and research settings to assess muscle function, diagnose neuromuscular disorders,and guide rehabilitation therapy. Over the years, EMG has evolved from a basic measurement tool into an essential technology within clinical and research environments, propelled by advances in recording techniques and digital innovations. The integration of wearable technology and artificial intelligence (AI) has significantly expanded its applications, particularly in rehabilitation and sports science. By capturing muscle electrical activity through surface or intramuscular electrodes, EMG benefits from enhanced signal processing that improves accuracy and data analysis. Despite challenges such as signal interference and the complexities of movement patterns—especially in wrist and hand rehabilitation—EMG combined with robotic systems offers real-time feedback for precise and personalized therapy. However, obstacles like cost, complexity, and variability among patients still remain. Future advancements aim to make EMG more accessible and to integrate AI for tailored rehabilitation strategies, alongside improvements in sensors and wireless communication to enhance reliability and performance. This review explores various facets of EMG,from its fundamental principles to its application in detecting disrupted wrist and hand movements through robotic approaches. It provides a comprehensive analysis of EMG’s historical and technological evolution, recent innovations like AI and wearable devices, and its extensive applications in rehabilitation and sports science. Detailed case studies illustrate its effectiveness in areas such as stroke recovery and spinal cord injury rehabilitation. Additionally, the review addresses challenges like technical limitations and patient variability while emphasizing the integration of EMG with robotic systems for personalized therapy. It also discusses the significance of real-time feedback, future enhancements in AI and sensor technology, and the pressing need for more affordable, user-friendly solutions to improve therapeutic outcomes.

Estimation of simultaneous equation models by backpropagation method using stochastic gradient descent

Simultaneous equation model (SEM) is an econometric technique traditionally used in economics but with many applications in other sciences. This model allows the bidirectional relationship between variables and a simultaneous relationship between the equation set. There are many estimators used for solving an SEM. Two-steps least squares (2SLS), three-steps least squares (3SLS), indirect least squares (ILS), etc. are some of the most used of them. These estimators let us obtain a value of the coefficient of an SEM showing the relationship between the variables. There are different works to study and compare the estimators of an SEM comparing the error in the prediction of the data, the computational cost, etc. Some of these works study the estimators from different paradigms such as classical statistics, Bayesian statistics, non-linear regression models, etc. This work proposes to assume an SEM as a particular case of an artificial neural networks (ANN), considering the neurons of the ANN as the variables of the SEM and the weight of the connections of the neurons the coefficients of the SEM. Thus, backpropagation method using stochastic gradient descent (SGD) is proposed and studied as a new method to obtain the coefficient of an SEM.

Investigating the Performance Optimization of Three‐Quantum‐Well High Electron Mobility Transistor Device: Analyzing Operational Characteristics and Gate Voltage Influence

Quantum well AlGaN/AlN/GaN high electron mobility transistor (HEMT) devices are a unique kind of semiconductor device that make use of a heterostructure made up of layers of different materials. In this study, the operational characteristics and performance optimization of HEMT devices, with a specific emphasis on the behavior of three‐quantum‐well (3QW) HEMT devices, are explored. The impact of gate voltage is explored and is observed that higher gate voltages result in lower pinch‐off voltages and higher saturation drain currents. In these findings, valuable insights for optimizing circuit designs and ensuring dependable operation across a range of electronic applications are offered. In addition, In this study, the device's ability to detect changes in gate voltage and its nonlinear characteristics is emphasized, providing valuable information for improving the device's performance. Additionally, the benefits of HEMT devices with negative threshold voltages are explored. Devices utilizing AlGaN/AlN/GaN structures demonstrate exceptional performance characteristics, such as rapid switching capabilities, high energy efficiency, and low noise performance, making them well suited for a wide range of scientific and technological applications. Finally, when comparing a QW HEMT device at different drain bias conditions (Vd = 1 V and Vd = 5 V), noticeable variations suggesting improved performance with higher drain bias.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

More quantum chemistry with fewer qubits

Quantum computation is one of the most promising new paradigms for the simulation of physical systems composed of electrons and atomic nuclei, with applications in chemistry, solid-state physics, materials science, and molecular biology. This requires a truncated representation of the electronic structure Hamiltonian using a finite number of orbitals. While it is, in principle, obvious how to improve on the representation by including more orbitals, this is usually unfeasible in practice (e.g., because of the limited number of qubits available) and severely compromises the accuracy of the obtained results. Here, we propose a quantum algorithm that improves on the representation of the physical problem by virtue of second-order perturbation theory. In particular, our quantum algorithm evaluates the second-order energy correction through a series of time-evolution steps under the unperturbed Hamiltonian. An important application is to go beyond the active-space approximation, allowing to include corrections of virtual orbitals, known as multireference perturbation theory. Here, we exploit the fact that the unperturbed Hamiltonian is diagonal for virtual orbitals and show that the number of qubits is independent of the number of virtual orbitals. This gives rise to more accurate energy estimates without increasing the number of qubits. Moreover, we demonstrate numerically for realistic chemical systems that the total runtime has highly favorable scaling in the number of virtual orbitals compared to previous studies. Numerical calculations confirm the necessity of the multireference perturbation theory energy corrections to reach accurate ground-state energy estimates. Our perturbation theory quantum algorithm can also be applied to symmetry-adapted perturbation theory. As such, we demonstrate that perturbation theory can help to reduce the quantum hardware requirements for quantum chemistry. Published by the American Physical Society 2024

A virtual 3D Chemistry Escape Room for Exam Preparations

Chemistry can be incredibly captivating. However, learning the theory can often be challenging and less appealing for students. Providing them with hands-on methods in their learning process is beneficial and also helps to lower the barrier for understanding theory. In a gamified approach, the "ChemScape" virtual 3D chemistry escape room, designed as a realistic laboratory with various equipment and hidden chemical tasks, can help solidify chemical knowledge and prepare students for chemistry exams. In 2023, as part of an e-learning project, the foundation of such a chemistry escape room was established, and several chemical questions were incorporated as escape room tasks. The objective is to discover hidden questions within the laboratory, utilize different instruments (such as scales), conduct virtual chemistry experiments, and solve various chemistry questions using theoretical knowledge (such as nomenclature and understanding of pH) in a game-based setting (utilizing multiple-choice, drag and drop, connecting cables, solving color codes, etc.). Typically, a code (comprising numbers, letters, signs, etc.) needs to be obtained for progression. To solve the chemistry tasks, approximately 10 different door-openers have been developed. Initial user tests have been conducted, and adjustments were made based on the feedback received, which was satisfactory and positive. In 2024, the "ChemScape" escape room will evolve from a game with a fixed set of tasks to a dynamic escape room game with a dynamic database that allows for the creation of new questions at the same stations, significantly expanding learning opportunities. A virtual chemistry laboratory rat provides hints if a participant gets stuck and requires assistance. Furthermore, "ChemScape" will be trialed in chemistry lectures and potentially utilized as an additional attraction at events held at the University of Applied Sciences.

Asbestos: Communicating the Health Issues Derived from Fibrous Minerals to Society

Asbestos, also known by its commercial name “amianthus”, has been widely used in various industries due to its unique properties. However, the extensive use of asbestos has had serious consequences for human health, most notably asbestosis, an irreversible chronic lung disease. Asbestosis increases the risk of lung cancer and malignant mesothelioma, both of which are fatal. Applied sciences such as microscopy (optical and scanning electron microscopy) and geochemistry have been fundamental in characterizing the mineral fibers of asbestos to understand its role in human health. We previously used these techniques to characterize these fibers; in this study, we explored the issues associated with asbestos and asbestosis, as well as the challenges facing science communication strategies in effectively informing society and workers about these risks. The lack of scientific culture, in general, has led to a lack of public awareness of the risks of asbestos. As such, effective communication and outreach plans and strategies, including the visualization of the fibers to demonstrate why problems arise if inhaled, must be implemented to address these challenges. Educational campaigns, guidelines, and plans that are informative and actionable, teaching workers, communities, and the public about the risks of asbestos are crucial. A general knowledge of mineralogy and geochemistry is needed, and providing and disseminating proper scientific communication may help to close the knowledge gap. We use examples and experience from Spain and Italy to illustrate this matter, as we have been working on the characterization of ultramafic complexes in these countries for more than ten years. Additionally, because these countries have strict laws for asbestos-containing materials, they are currently involved in retiring and demolishing buildings and infrastructure that contain asbestos.

Dynamics of particle entrainment for glass particles suspended in various fluids

The frictional properties of two sliding surfaces are often influenced by a fluid lubricant’s ability to separate and facilitate movement. When particles are present, their entrainment between surfaces can alter friction, either increasing or decreasing it compared to fluid lubrication alone. Understanding the frictional regimes and the dynamics driving particle entrainment in suspensions is thus critical. This study investigates the tribological properties of glass particles suspended in various fluid matrices. We examined suspensions with varying particle concentrations, fluid viscosities, fluid hydrophobicity, and particle hydrophobicity. Our findings reveal that particle lubrication dominates under the following conditions: (I) high particle concentrations, (II) high fluid viscosities, (III) strong particle – surface interactions, and (IV) weak fluid – particle interactions. These insights are crucial for applications in food science, biomedical industries, and pharmaceuticals, where controlling particle friction is essential for optimizing consumer satisfaction and ensuring the performance and safety of lubricants in medical devices.

Success and opportunities of the American Academy of Pediatrics Marshall Klaus research grant program in neonatal-perinatal medicine.

Physician-scientists are a crucial link between clinical practice and research. The American Academy of Pediatrics (AAP) initiated the Marshall Klaus Perinatal Research Award to enhance the development of research skills among physicians training in Neonatal-Perinatal Medicine. In this study, we sought to identify trends in funding along with geographical and demographic variables of the applicants and mentees and assess the applicants' scholarly productivity and funding from the National Institutes of Health (NIH). We reviewed the data of applicants and awardees from 2015-2024. We found that basic science applications had a higher funding likelihood than clinical/translational applications. The geographical distribution of awardees is skewed. There was a significant association between awardee status and K08 or K23 funding attainment. Future efforts should support more equitable award distribution and a diverse research landscape in neonatal-perinatal medicine.

Simulating the ionic liquid mixing with organic-solvent clarifies the mixture’s SFG spectral behavior and the specific surface region originating SFG

Molecular dynamics (MD) simulation of the green ionic liquid [C₄mim][PF₆] mixed with polar benzonitrile (BNZ) solvent provides detailed insights into their structural and dynamic properties, essential for electrochemistry and materials science applications. The simulations we carried out at varying mole fractions (XBZN) reveal the mixtures’ physical, structural, and dynamic properties, with radial, spatial, and combined distribution functions, highlighting the effective interaction through H-bonding involved. The simulation indicates that BZN stacks on the cation butyl tail, providing a significant explanation for the unique experimental observations (following). Adding BZN causes the mixture’s liquid dynamics to increase linearly at low XBZN and exponentially at high XBZN, with a notable singular transition at 0.5XBZN. Comprehensive efforts were made to verify and support experimental sum frequency generation (SFG) spectroscopy by simulating the surface structure of the mixtures. Consequently, the simulated BZN stacking structure explains (1) the absence of the C≡N vibrational mode in the SFG spectrum for XBZN < 0.8, and (2) the gradual diminishing of the CH3 SFG signal, which disappears as XBZN approaches 0.5. Finally, this research removes a persistent ambiguity, proving that only the molecular moieties on the surface generate the SFG vibrational signal, while those in the subsurface do not.

High power continuous-wave, Q-switched, and quasi-continuous-wave operation of a diode-pumped Tm,Ho:YAG laser oscillator at 2.09 μm

A high-power diode-pumped 2.09 μm Tm,Ho:YAG laser oscillator with multi-regime operation is demonstrated. In the continuous-wave (CW) regime, it delivers 44.68 W output power with M2 = 3.47, yielding a record-high brightness of 87.57 MW/cm2·sr. In the Q-switched mode, stable laser performance across pulse repetition frequencies (PRFs) from 1 kHz to 4 kHz is achieved. At a PRF of 1 kHz, single pulse energy reaches 22.85 mJ with a pulse width of 443.4 ns and M2 = 3.50. In the quasi-continuous-wave (QCW) operation, a pulse energy of 31.85 mJ is obtained at 200 Hz with a 370 μs pulse width and M2 = 4.49. These pulse energies are the highest reported to date for Tm-Ho co-doped laser oscillators at high PRF. This also marks the first demonstration of a versatile 2 μm laser that can operate at room temperature while delivering high-energy, high-repetition-rate pulses in both nanosecond and microsecond durations within a single device, making it highly appealing for scientific and industrial applications.

Noise sensors - Part 1: Usage for the redesign of neighbourhoods

An interdisciplinary research project at the Stuttgart University of Applied Sciences with participation of the departments of urban planning, surveying and geoinformatics, business psychology and acoustics, present alternative urban scenarios. Different aspects for the redesign of the district, especially measures to reduce the individual motorised transport, were investigated. Virtual reality and augmented reality experiments are used in public spaces, to show redesign possibilities. Surveys of people, who had the experience of the future scenarios via the AR application and VR application, were carried out. Additionally, acoustical surveys and measurements in this research project were performed using so called noise sensors. These sensors based on MEMS microphones were developed for a simple and cost-effective measurement of external noise in a citizen sciences project. The measurement results of the noise sensors are saved online and can be accessed on the homepage "Sensor.Community". One of the noise sensors was used in the project area over a longer period. Measurement values were generated in third octave bands at intervals of 5 s and are available for further evaluations, e.g. for the calculation of indoor noise levels. The paper presents the research project and reports about the application of the noise sensor.

Experience with the high mobility reception plate for source characterization according to EN 15657

EN 12354-5 describes methods for the prediction of noise from technical equipment based on the airborne sound radiation and the structure-borne sound excitation. This paper considers methods to characterize the source in terms of structure-borne-sound (sbs) emission. Laboratory methods for sbs source characterization are given in EN 15657. In current research and development projects at Rosenheim Technical University of Applied Sciences these methods are applied to fully characterize technical equipment such as heat pumps or ventilation systems. This includes the determination of the equivalent blocked force, the source single equivalent free velocity and the source single equivalent mobility. EN 15657 describes various methods to determine the required properties. Recent project experience showed that this is essentially necessary because the most suitable method has to be applied depending on the nature of the technical equipment. This paper presents experiences with indirect measurement methods on the high-mobility plate and the direct measurement method given in EN 15657.

Acoustic waves propagating through a fluid area containing randomly distributed coated cylinders

The research investigates acoustic wave propagation through a fluid region containing randomly arranged cylinders coated in identical materials, resulting in multiple scattering and the emergence of an effective wave. It examines the impact of the coating's viscoelasticity, cylinder material, and scatterer volume fraction on this wave. Additionally, it analyzes dispersion and attenuation as acoustic waves interact with the fluid region containing scatterers, particularly emphasizing normal incidence. This comprehensive study advances the understanding of wave scattering in random media, shedding light on this complex phenomenon. By delving into the complex interplay between wave behavior and material characteristics, the research provides valuable insights that can inform various acoustics and materials science applications, contributing to advancements in these fields.

Octave-spanning supercontinuum coherent soft x-ray for producing a single-cycle soft x-ray pulse.

This study demonstrates the potential to generate a soft x-ray single-cycle attosecond pulse using a single-cycle mid-infrared pulse from advanced dual-chirped optical parametric amplification (DC-OPA). A super continuum high harmonic (HH) spectrum was generated in argon (80-160 eV) and neon (150-270 eV). The experimental spectra reasonably agree with those calculated by the strong-field approximation model and Maxwell's equations. In addition, simulation results indicate that the dispersion of HHs in argon can be compensated using a 207-nm Zr filter to obtain 40 as (Fourier transform limited (FTL)) pulses (1.1 cycles at 118 eV). For neon, a 278-nm Sn filter can compensate for the dispersion of HH and create 23 as FTL pulses (1.1 cycles at 206 eV). This soft x-ray single-cycle attosecond pulse is expected to be highly valuable for ultrafast science and applications in quantum information science.

Case-crossover designs and overdispersion with application to air pollution epidemiology.

Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.

The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R2 with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications.

A Paired-Ion Framework Composed of Vanadyl Porphyrin Molecular Qubits Extends Spin Coherence Times.

Molecular electron spin qubits arranged in precise arrays have great potential for use in quantum information science applications. Molecular qubits are synthetically versatile and can be placed in ordered arrangements upon incorporation into a new class of materials known as paired-ion frameworks (PIFs). A PIF composed of vanadyl porphyrin molecular qubits, VOTCPP-PIF-1, was synthesized as single crystals. Electron paramagnetic resonance spectroscopy was used to study their spin coherence at temperatures up to 293 K. A suspension of VOTCPP-PIF-1 at 5 K in dimethylformamide (DMF) had a spin-spin relaxation time (Tm) of 270 ns. In DMF-d7 and at 5 K, the coherence time of this material increased to 370 ns. This increase in Tm is attributed to the lower gyromagnetic ratio of 2H compared to 1H, which results in weaker electron-nuclear dipolar coupling that reduces the effect of nuclear spin flips on electron spin coherence. In toluene, crystals of VOTCPP-PIF-1 had a Tm of 31 ns at 293 K, demonstrating that PIFs are a promising platform for creating materials for quantum information science applications.