Example Of Application Research Articles

A Region-Based Approach for the Operational Reliability Evaluation of Power Systems With Renewable Energy Integration

Power system operational reliability evaluation is computationally time-consuming due to the need to analyze numerous system states resulting from uncertain renewable energies and loads as well as random outages of components. This paper presents a load feasible region (LFR) approach to accelerate the process of operational reliability evaluation. The LFR of each state, which is defined as a polyhedron in the load space, can be built in advance offline. The inner points of the LFR correspond to the loads that can be satisfied under the system operational constraints. Then, the preestablished LFR can be corrected with little computing effort considering the renewable energy fluctuation during the operation phase. The optimal load shedding under each state can be obtained directly by calculating the minimum Manhattan distance to the LFR's boundary. Multiple application examples are presented to demonstrate the efficiency and scalability of the proposed region-based approach.

Observation of solar radio burst events from Mars orbit with the Shallow Radar instrument

Context. Multispacecraft and multiwavelength observations of solar eruptions, such as flares and coronal mass ejections, are essential to understanding the complex processes behind these events. The study of solar burst events in the radio frequency spectrum has relied almost exclusively on data from ground-based observations and a few dedicated heliophysics missions such as STEREO or Wind. Aims. By reanalysing existing data from the Mars Reconnaissance Orbiter (MRO) Shallow Radar (SHARAD) instrument, a Martian planetary radar sounder, we discovered the instrument was also capable of detecting solar radio bursts and that it was able to do so with unprecedented resolution for a space-based solar instrument. In this study, we aim to demonstrate the reliability and value of SHARAD as a new solar radio observatory. Methods. We characterised the sensitivity of the instrument to type III solar radio bursts through a statistical analysis of correlated observations using STEREO and Wind as references. Using 38 correlated detections, we established the conditions under which SHARAD can observe solar bursts in terms of acquisition geometry. As an example of scientific application, we also present the first analysis of type III characteristic times at high resolution beyond 1 AU. Results. A simple logistic model based purely on geometrical acquisition parameters can predict burst show versus no-show in SHARAD data with an accuracy of 79.2%, demonstrating the reliability of the instrument in detecting solar bursts and laying the foundation for using SHARAD as a solar radio observatory. The extremely high resolution of the instrument, both in temporal and frequency directions; its bandwidth; and its position in the Solar System enable SHARAD to make significant contributions to heliophysics. Notably, it could provide data on plasma processes on the site of the burst generation and along the propagation path of associated fast electron beams.

ChatGPT als Unterstützung von Lehrkräften – Einordnung, Analyse und Anwendungsbeispiele

ZusammenfassungSeit seiner Veröffentlichung gewinnt das Large Language Model (Großes Sprachmodell) ChatGPT zunehmend an Bedeutung in der deutschen Bildungslandschaft. Während Schülerinnen und Schüler dieses Large Language Model bereits aktiv nutzen, herrscht unter Lehrkräften noch Uneinigkeit darüber, wie ChatGPT sie effektiv in ihrer alltäglichen Arbeit unterstützen kann. Mithilfe einer qualitativen Untersuchung werden verschiedene Aufgabenbereiche von Lehrkräften anhand konkreter Beispiele veranschaulicht und analysiert, wie ChatGPT in diesen Bereichen Unterstützung bieten kann – und in welchen der Einsatz von ChatGPT weniger geeignet ist. Durch das Large Language Model können Anregungen für alle identifizierten Aufgabenbereiche gegeben werden. Große Potenziale werden bei routinemäßigen Aufgaben, wie beispielsweise der schnellen Zusammenfassung von Informationen, gesehen. ChatGPT sollte eher nicht für pädagogische Aufgaben eingesetzt werden, wie z. B. die Arbeit mit herausforderndem Verhalten. Da Künstliche Intelligenz-basierte Tools wie ChatGPT in Zukunft voraussichtlich weiter an Bedeutung gewinnen werden, ist es notwendig, Lehrkräften eine konkrete Hilfestellung zur effektiven Nutzung dieser Tools zu geben. Dementsprechend wird in diesem Artikel ein Leitfaden hinsichtlich der Nutzung von ChatGPT im Unterricht durch Lehrkräfte bereitgestellt. Diese umfassen unter anderem die Bereiche Prompting, Interpretation, Reflexion und Datenschutz und liefern Lehrkräften so eine wertvolle Unterstützung bei dem Einsatz von ChatGPT.

Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

AbstractScale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a novel scale- invariant neural network. Instead of standard 2d or 3d convolutions for combining spatial information, the Riesz network is based on the Riesz transform which is a scale-equivariant operation. As a consequence, this network naturally generalizes to unseen or even arbitrary scales in a single forward pass. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. In this context, ‘scale’ refers to the crack thickness which may vary strongly even within the same sample. To prove its scale invariance, the Riesz network is trained on one fixed crack width. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths. An additional experiment is carried out on the MNIST Large Scale data set.

Proportional integral observer‐based robust fault‐tolerant consensus for nonlinear two‐time‐scale‐agent systems with heterogeneous sensor faults

AbstractThis article studies the fault‐tolerant consensus issue of a class of nonlinear two‐time‐scale‐agent systems with heterogeneous sensor faults and external disturbances. First, the decentralized proportional integral (PI) observer for each agent is constructed to estimate the states and sensor faults simultaneously. Then, a distributed adaptive protocol based on the decentralized PI observer is designed to guarantee the consensus of the underlying systems. Some sufficient existence conditions are given in terms of linear matrix inequalities (LMIs) for the decentralized PI observer and the fault tolerant consensus protocol. Note that performance index is employed to attenuate the influence of sensor faults and disturbances on state estimation and the consensus of the underlying systems. Finally, the effectiveness and superiority of the proposed method are demonstrated by an application example and a numerical example.

FCSG-Miner: Frequent closed subgraph mining in multi-graphs

Graph data-based mining is vital in various fields, such as business management, chemistry, and social networks. Frequency-based frameworks have limitations regarding large mining output. In many real-world scenarios, not all frequent patterns hold significant meaning. To address this issue, concise representation such as closed patterns is proposed. Unfortunately, existing methods utilize support or occurrence as frequency measurements, which have drawbacks. Support overlooks the isomorphic quantity of a single graph, while occurrence lacks the downward closure property. In this paper, we introduce an approach for mining frequent closed subgraphs. A novel measure MMNI (multiple minimum node image) is introduced to strike a balance between support and occurrence measures. Additionally, we design a novel structure to store occurrence information. We develop a pruning strategy and employ an early termination strategy to enhance efficiency. To evaluate the performance of our algorithm, we conduct experiments on seven real datasets, considering four aspects. The results demonstrate that our algorithm has high efficiency and performance compared to the state-of-the-art algorithm for closed subgraph mining. In several cases, our method requires only 50% of the time consumed by previous approaches. We also present a real-world application example in the domain of bike-sharing systems.

Modelling the Deformation of Polydomain Liquid Crystal Elastomers as a State of Hyperelasticity

AbstractA hyperelasticity modelling approach is employed for capturing various and complex mechanical behaviours exhibited by macroscopically isotropic polydomain liquid crystal elastomers (LCEs). These include the highly non-linear behaviour of nematic-genesis polydomain LCEs, and the soft elasticity plateau in isotropic-genesis polydomain LCEs, under finite multimodal deformations (uniaxial and pure shear) using in-house synthesised acrylate-based LCE samples. Examples of application to capturing continuous softening (i.e., in the primary loading path), discontinuous softening (i.e., in the unloading path) and auxetic behaviours are also demonstrated on using extant datasets. It is shown that our comparatively simple model, which breaks away from the neo-classical theory of liquid crystal elastomers, captures the foregoing behaviours favourably, simply as states of hyperelasticity. Improved modelling results obtained by our approach compared with the existing models are also discussed. Given the success of the considered model in application to these datasets and deformations, the simplicity of its functional form (and thereby its implementation), and comparatively low(er) number of parameters, the presented isotropic hyperelastic strain energy function here is suggested for: (i) modelling the general mechanical behaviour of LCEs, (ii) the backbone in the neo-classical theory, and/or (iii) the basic hyperelastic model in other frameworks where the incorporation of the director, anisotropy, viscoelasticity, temperature, softening etc parameters may be required.

Application of AES and 3DES Hybrid Encryption Algorithm Based on Optimized S Box in the Data Transmission of Human Resources and Social Security Informatization

In this paper, a hybrid encryption algorithm based on AES and 3DES is proposed to improve the security of data transmission in human resources and social security informatization. We first analyze the advantages and disadvantages of AES and 3DES, and then elaborate on why the two algorithms are used in combination. Then, this paper discusses the implementation process and mathematical principle of the hybrid algorithm. Finally, we implement the hybrid algorithm in the C + + language and present an application example.

Current study on HCI based AR technology in the medical industry

Augmented Reality (AR) is a new technology that based on Human-Computer Interaction (HCI) can combine our real life with 3D virtual information through real-time computing and multiple sensors. It simulates people's senses, such as hearing, vision, touch, etc., bringing things that cannot be seen in reality to people's eyes, which will bring people a feeling beyond reality. It has already appeared in our lives and has applications in many fields, such as architecture. Especially in the gaming field, AR, an innovative emerging technology, is widely loved by young people. With the development of technology, the forms of AR games have also become diverse. It has also developed rapidly in the medical field in recent years. This article will discuss the current situation of AR and its advantages in the medical industry based on Human-Computer Interaction (HCI) from three aspects: doctor diagnosis, patient rehabilitation, and medical education with examples of applications. Then, this article will discuss some of the drawbacks of using AR and the problems that will be encountered in future development, and propose solutions. Finally, analysis and predictions will be made for the future development of AR in the industry.

Separation of signal and harmonics in non-explosive seismic prospecting with amplitude and nonlinear frequency-modulated signals

When vibroseis oscillations are excited, along with the main signal, harmonics are generated. They travel into the Earth and, like the main signal, interact with the target reflectors. Harmonics have a wider than that of the main signal frequency band, so they can be used to increase the resolution of the seismic data. To do this, the signal-related data should be separated from the harmonic-related data. This problem can be successfully solved by the previously proposed optimization recursive filtering algorithm, which, however, was developed only for linearly frequency-modulated signals. In this work, the algorithm is generalized to the case of amplitude modulation and nonlinear frequency modulation. Examples of application of the technique to increase the resolution of real vibroseis data are given.

A Bayesian optimization R package for multitrait parental selection.

Selecting and mating parents in conventional phenotypic and genomic selection are crucial. Plant breeding programs aim to improve the economic value of crops, considering multiple traits simultaneously. When traits are negatively correlated and/or when there are missing records in some traits, selection becomes more complex. To address this problem, we propose a multitrait selection approach using the Multitrait Parental Selection (MPS) R package-an efficient tool for genetic improvement, precision breeding, and conservation genetics. The package employs Bayesian optimization algorithms and three loss functions (Kullback-Leibler, Energy Score, and Multivariate Asymmetric Loss) to identify parental candidates with desirable traits. The software's functionality includes three main functions-EvalMPS, FastMPS, and ApproxMPS-catering to different data availability scenarios. Through the presented application examples, the MPS R package proves effective in multitrait genomic selection, enabling breeders to make informed decisions and achieve strong performance across multiple traits.

Recycled Waste as Polyurethane Additives or Fillers: Mini-Review.

The intensive development of the polyurethanes industry and limited resources (also due to the current geopolitical situation) of the raw materials used so far force the search for new solutions to maintain high economic development. Implementing the principles of a circular economy is an approach aimed at reducing the consumption of natural resources in PU production. This is understood as a method of recovery, including recycling, in which waste is processed into PU, and then re-used and placed on the market in the form of finished sustainable products. The effective use of waste is one of the attributes of the modern economy. Around the world, new ways to process or use recycled materials for polyurethane production are investigated. That is why innovative research is so important, in which development may change the existing thinking about the form of waste recovery. The paper presents the possibilities of recycling waste (such as biochar, bagasse, waste lignin, residual algal cellulose, residual pineapple cellulose, walnut shells, silanized walnut shells, basalt waste, eggshells, chicken feathers, turkey feathers, fiber, fly ash, wood flour, buffing dust, thermoplastic elastomers, thermoplastic polyurethane, ground corncake, Tetra Pak®, coffee grounds, pine seed shells, yerba mate, the bark of Western Red Cedar, coconut husk ash, cuttlebone, glass fibers and mussel shell) as additives or fillers in the formulation of polyurethanes, which can partially or completely replace petrochemical raw materials. Numerous examples of waste applications of one-component polyurethanes have been given. A new unexplored niche for the research on waste recycling for the production of two components has been identified.

Deep Learning in Image-Based Plant Phenotyping.

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessment of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges. Expected final online publication date for the Annual Review of Plant Biology, Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo

This paper contains the foundation for a new Particle-In-Cell model for gas discharges, based on Îto diffusion and Kinetic Monte Carlo (KMC). In the new model the electrons are described with a microscopic drift-diffusion model rather than a macroscopic one. We discuss the connection of the Îto-KMC model to the equations of fluctuating hydrodynamics and the advection-diffusion-reaction equation which is conventionally used for simulating streamer discharges. The new model is coupled to a particle description of photoionization, providing a non-kinetic all-particle method with several attractive properties, such as: 1) Taking the same input as a fluid model, e.g. mobility coefficients, diffusion coefficients, and reaction rates. 2) Guaranteed non-negative densities. 3) Intrinsic support for reactive and diffusive fluctuations. 4) Exceptional stability properties. The model is implemented as a particle-mesh model on cut-cell grids with Cartesian adaptive mesh refinement. Positive streamer discharges in atmospheric air are considered as the primary application example, and we demonstrate that we can self-consistently simulate large discharge trees.

Designing a structure-function alphabet of helix based on reduced amino acid clusters

Several simple secondary structures could form complex and diverse functional proteins, meaning that secondary structures may contain a lot of hidden information and are arranged according to certain principles, to carry enough information of functional specificity and diversity. However, these inner information and principles have not been understood systematically. In our study, we designed a structure-function alphabet of helix based on reduced amino acid clusters to describe the typical features of helices and delve into the information. Firstly, we selected 480 typical helices from membrane proteins, zymoproteins, transcription factors, and other proteins to define and calculate the interval range, and the helices are classified in terms of hydrophilicity, charge and length: (1) hydrophobic helix (≤43%), amphiphilic helix (43%∼71%), and hydrophilic helix (≥71%). (2) positive helix, negative helix, electrically neutral helix and uncharged helix. (3) short helix (≤8 aa), medium-length helix (9–28 aa), and long helix (≥29 aa). Then, we designed an alphabet containing 36 triplet codes according to the above classification, so that the main features of each helix can be represented by only three letters. This alphabet not only preliminarily defined the helix characteristics, but also greatly reduced the informational dimension of protein structure. Finally, we present an application example to demonstrate the value of the structure-function alphabet in protein functional determination and differentiation.

Usefulness of fat-containing agents: an initial study on estimating fat content for magnetic resonance imaging.

This initial study aimed at testing whether fat-containing agents can be used for the fat mass estimation methods using magnetic resonance imaging (MRI). As an example for clinical application, fat-containing agents (based on soybean oil, 10% and 20%), 100% soybean oil, and saline as reference substances were placed outside the proximal femurs obtained from 14 participants and analyzed by 0.3T MRI. Fat content was the estimated fat fraction (FF) based on signal intensity (SIeFF, %). The SIeFF values of the femoral bone marrow, including the femoral head, neck, shaft, and trochanter area, were measured. MRI data were compared in terms of bone mineral content (BMC) and bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) in the proximal femur. Twelve pig femurs were also used to confirm the correlation between FF by the DIXON method and SIeFF. According to Pearson's correlation coefficient, the SIeFF and total BMC and BMD data revealed strong and moderate negative correlations in the femoral head (r < - 0.74) and other sites (r = - 0.66 to - 0.45). FF and SIeFF showed a strong correlation (r = 0.96). This study was an initial investigation of a method for estimating fat mass with fat-containing agents and showed the potential for use in MRI. SIeFF and FF showed a strong correlation, and SIeFF and BMD and BMC showed correlation; however, further studies are needed to use SIeFF as a substitute for DXA.

Exponential rate of decay of correlations of equilibrium states associated with non-uniformly expanding circle maps

In the context of expanding maps of the circle with an indifferent fixed point, understanding the joint behavior of dynamics and pairs of moduli of continuity (ω,Ω) may be a useful element for the development of equilibrium theory. Here we identify a particular feature of modulus Ω (precisely limx→0+supdΩ(dx)/Ω(d)=0 ) as a sufficient condition for the system to exhibit exponential decay of correlations with respect to the unique equilibrium state associated with a potential having ω as modulus of continuity. This result is derived from obtaining the spectral gap property for the transfer operator acting on the space of observables with Ω as modulus of continuity, a property that, as is well known, also ensures the Central Limit Theorem. Examples of application of our results include the Manneville–Pomeau family.

The Meaning and Accuracy of the Improving Functions in the Solution of the CBQR by Krotov’s Method

A new solution to the continuous-time bilinear quadratic regulator optimal control problem (CBQR) was recently developed using Krotov’s Method. This paper provides two theoretical results related to the properties of that solution. The first discusses the equivalent representation of the cost-to-go performance index. The second one breaks down this equivalence into smaller identities referencing the components of the performance index. The paper shows how these results can be used to verify the numerical accuracy of the computed solution. Additionally, the meaning of the improving function and the equivalent representation, which are the main elements in the discussed CBQR’s solution, are explained according to the derived notions. A numerical example of structural control application exemplifies the significance of these results and how they can be applied to a specific CBQR problem.

Using thermal response factors with time dependent thermal properties

This paper presents a simple and fast methodology to consider changing thermal properties in, for example, the design of shallow geothermal systems, by using thermal response factors. The simulation period is split into multiple sections according to the changes in thermal properties. By transforming the temperature response from one section to the next and subsequent superposition, any changes in the thermal conductivity properties of the media through which heat travels can be taken into account. The method is verified by numerical simulation and its efficiency is demonstrated in an application example. Results show that even though the computational effort increases exponentially with the variation of thermal parameters in time, the computational time is significantly shorter than comparable numerical simulations.

Design rules applied to silver nanoparticles synthesis: A practical example of machine learning application.

The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In this study, we manually extracted synthesis-related data from the literature and leveraged various machine learning algorithms. Data extraction included parameters such as reactant concentrations, experimental conditions, as well as physicochemical properties. The antibacterial efficiencies and toxicological profiles of the synthesized nanoparticles were also extracted. In a second step, based on data completeness, we employed regression algorithms to establish relationships between synthesis parameters and desired endpoints and to build predictive models. The models for core size and antibacterial efficiency were trained and validated using a cross-validation approach. Finally, the features’ impact was evaluated via Shapley values to provide insights into the contribution of features to the predictions. Factors such as synthesis duration, scale of synthesis and the choice of capping agents emerged as the most significant predictors. This study demonstrated the potential of machine learning to aid in the rational design of synthesis process and paves the way for the safe-by-design principles development by providing insights into the optimization of the synthesis process to achieve the desired properties. Finally, this study provides a valuable dataset compiled from literature sources with significant time and effort from multiple researchers. Access to such datasets notably aids computational advances in the field of nanotechnology.