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The development of an ecosystem that balances consumer convenience and security is imperative given the expanding market for electric vehicles (EVs). The vast amount of data that EV charging station management systems (EVCSMSs) give is powered by the Internet of Things (IoT) ecosystem. Intrusion Detection Systems (IDSs), which track network traffic to spot potentially dangerous data exchanges in IT and IoT contexts, are constantly improving in terms of efficacy and accuracy. Intrusion detection is becoming a major topic in academia because of the acceleration of IDS development caused by machine learning and deep learning techniques. The goal of the research presented in this paper is to use a machine-learning-based intrusion detection system with low false-positive rates and high accuracy to safeguard the ecosystem of EV charging stations (EVCS).

Lithium-ion batteries (LiBs) have transformed electrochemical energy storage technologies and made a substantial contribution to grid-scale energy storage and the e-mobility revolution. Notwithstanding their many benefits, safety issues specifically, thermal runaway incidents have drawn attention from all around the world. In addition to discussing safety concerns, cooling techniques, and the history of battery materials, this study offers a thorough analysis of the growth, difficulties, and developments in Li-ion battery technology. Quantum Espresso software has been used to compute the magnetic characteristics of several potential Li-ion battery materials, which can enhance Li-ion battery performance.

Test smells, similar to code smells, can negatively impact both the test code and the production code being tested. Despite extensive research on test smells in languages like Java, Scala, and Python, automated tools for detecting test smells in C# are lacking. This paper aims to bridge this gap by extending the study of test smells to C#, and developing a tool (xNose) to identify test smells in this language and analyze their distribution across projects. We identified 16 test smells from prior studies that were language-independent and had equivalent features in C# and evaluated xNose, achieving a precision score of 96.97% and a recall score of 96.03%. In addition, we conducted an empirical study to determine the prevalence of test smells in xUnit-based C# projects. This analysis sheds light on the frequency and distribution of test smells, deepening our understanding of their impact on C# projects and test suites. The development of xNose and our analysis of test smells in C# code aim to assist developers in maintaining code quality by addressing potential issues early in the development process., Comment: Full report of our ICSE'24 poster

Ovarian cancer (OC) is one of the most prevalent types of cancer in women. Early and accurate diagnosis is crucial for the survival of the patients. However, the majority of women are diagnosed in advanced stages due to the lack of effective biomarkers and accurate screening tools. While previous studies sought a common biomarker, our study suggests different biomarkers for the premenopausal and postmenopausal populations. This can provide a new perspective in the search for novel predictors for the effective diagnosis of OC. Lack of explainability is one major limitation of current AI systems. The stochastic nature of the ML algorithms raises concerns about the reliability of the system as it is difficult to interpret the reasons behind the decisions. To increase the trustworthiness and accountability of the diagnostic system as well as to provide transparency and explanations behind the predictions, explainable AI has been incorporated into the ML framework. SHAP is employed to quantify the contributions of the selected biomarkers and determine the most discriminative features. A hybrid decision support system has been established that can eliminate the bottlenecks caused by the black-box nature of the ML algorithms providing a safe and trustworthy AI tool. The diagnostic accuracy obtained from the proposed system outperforms the existing methods as well as the state-of-the-art ROMA algorithm by a substantial margin which signifies its potential to be an effective tool in the differential diagnosis of OC.

Recently it has been demonstrated that the cosine chirp microwave pulse (CCMP) is capable of achieving fast and energy-efficient magnetization-reversal of a nanoparticle with zero-Temperature. However, we investigate the finite temperature, $T$ effect on the CCMP-driven magnetization reversal using the framework of the stochastic Landau Lifshitz Gilbert equation. At finite Temperature, we obtain the CCMP-driven fast and energy-efficient reversal and hence estimate the maximal temperature, $T_{max}$ at which the magnetization reversal is valid. $T_{max}$ increases with increasing the nanoparticle cross-sectional area/shape anisotropy up to a certain value, and afterward $T_{max}$ decreases with the further increment of nanoparticle cross-sectional area/shape anisotropy. This is because of demagnetization/shape anisotropy field opposes the magnetocrystalline anisotropy, i.e., reduces the energy barrier which separates the two stable states. For smaller cross-sectional area/shape anisotropy, the controlling parameters of CCMP show decreasing trend with temperature. We also find that with the increment easy-plane shape-anisotropy, the required initial frequency of CCMP significantly reduces. For the larger volume of nanoparticles, the parameters of CCMP remains constant for a wide range of temperature which are desired for the device application. Therefore, The above findings might be useful to realize the CCMP-driven fast and energy-efficient magnetization reversal in realistic conditions.

Sea surface temperature (SST) is an essential climate variable that can be measured via ground truth, remote sensing, or hybrid model methodologies. Here, we celebrate SST surveillance progress via the application of a few relevant technological advances from the late 20th and early 21st century. We further develop our existing water cycle observation framework, Flux to Flow (F2F), to fuse AMSR-E and MODIS into a higher resolution product with the goal of capturing gradients and filling cloud gaps that are otherwise unavailable. Our neural network architecture is constrained to a deep convolutional residual regressive neural network. We utilize three snapshots of twelve monthly SST measurements in 2010 as measured by the passive microwave radiometer AMSR-E, the visible and infrared monitoring MODIS instrument, and the in situ Argo dataset ISAS. The performance of the platform and success of this approach is evaluated using the root mean squared error (RMSE) metric. We determine that the 1:1 configuration of input and output data and a large observation region is too challenging for the single compute node and dcrrnn structure as is. When constrained to a single 100 x 100 pixel region and a small training dataset, the algorithm improves from the baseline experiment covering a much larger geography. For next discrete steps, we envision the consideration of a large input range with a very small output range. Furthermore, we see the need to integrate land and sea variables before performing computer vision tasks like those within. Finally, we see parallelization as necessary to overcome the compute obstacles we encountered.

We investigate the magnetic domain wall (DW) dynamics in uniaxial/biaxial nanowires under a thermal gradient (TG). The findings reveal that the DW propagates toward the hotter region in both nanowires. The main physics of such observations is the magnonic angular momentum transfer to the DW. The hard (shape) anisotropy exists in biaxial nanowire, which contributes an additional torque, hence DW speed is larger than that in uniaxial nanowire. With lower damping, the DW velocity is smaller and DW velocity increases with damping which is opposite to usual expectation. To explain this, it is predicted that there is a probability to form the standing spin-waves (which do not carry net energy/momentum) together with travelling spin-waves if the propagation length of thermally-generated spin-waves is larger than the nanowire length. For larger-damping, DW decreases with damping since the magnon propagation length decreases. Therefore, the above findings might be useful in realizing the spintronic (racetrack memory) devices.

We investigate the influence of shape anisotropy on the magnetization reversal of a single-domain magnetic nanoparticle driven by a circularly polarized linear down-chirp microwave field pulse (DCMP). Based on the Landau-Lifshitz-Gilbert equation, numerical results show that the three controlling parameters of DCMP, namely, microwave amplitude, initial frequency and chirp rate, decrease with the increase of shape anisotropy. For certain shape anisotropy, the reversal time significantly reduces. These findings are related to the competition of shape anisotropy and uniaxial magnetocrystalline anisotropy and thus to the height of energy barrier which separates the two stable states. The result of damping dependence of magnetization reversal indicates that for a certain sample shape, there exists an optimal damping situation at which magnetization is fastest. Moreover, it is also shown that the required microwave field amplitude can be lowered by applying the spin-polarized current simultaneously. The usage of an optimum combination of both microwave field pulse and current is suggested to achieve cost efficiency and faster switching. So these findings may provide the knowledge to fabricate the shape of a single domain nanoparticle for the fast and power-efficient magnetic data storage device.

The recent demonstration of MoSi2N4 and its exceptional stability to air, water, acid, and heat has generated intense interest in this family of two-dimensional (2D) materials. Among these materials, NbSi2N4, VSi2N4, and VSi2P4 are semiconducting, easy-plane ferromagnets with negligible in-plane magnetic anisotropy. They thus satisfy a necessary condition for exhibiting a dissipationless spin superfluid mode. The Curie temperatures of monolayer VSi2P4 and VSi2N4 are determined to be above room temperature based on Monte Carlo and density functional theory calculations. The magnetic moments of VSi2N4 can be switched from in-plane to out-of-plane by applying tensile biaxial strain or electron doping.

We investigate the magnetization reversal of single-domain magnetic nanoparticle driven by the circularly polarized cosine chirp microwave pulse (CCMP). The numerical findings, based on the Landau-Lifshitz-Gilbert equation, reveal that the CCMP is by itself capable of driving fast and energy-efficient magnetization reversal. The microwave field amplitude and initial frequency required by a CCMP are much smaller than that of the linear down-chirp microwave pulse. This is achieved as the frequency change of the CCMP closely matches the frequency change of the magnetization precession which leads to an efficient stimulated microwave energy absorption (emission) by (from) the magnetic particle before (after) it crosses over the energy barrier. We further find that the enhancement of easy-plane shape anisotropy significantly reduces the required microwave amplitude and the initial frequency of CCMP. We also find that there is an optimal Gilbert damping for fast magnetization reversal. These findings may provide a pathway to realize the fast and low-cost memory device.

In recent years deep neural networks have become the workhorse of computer vision. In this paper, we employ a deep learning approach to classify footwear impression's features known as \emph{descriptors} for forensic use cases. Within this process, we develop and evaluate an effective technique for feeding downsampled greyscale impressions to a neural network pre-trained on data from a different domain. Our approach relies on learnable preprocessing layer paired with multiple interpolation methods used in parallel. We empirically show that this technique outperforms using a single type of interpolated image without learnable preprocessing, and can help to avoid the computational penalty related to using high resolution inputs, by making more efficient use of the low resolution inputs. We also investigate the effect of preserving the aspect ratio of the inputs, which leads to considerable boost in accuracy without increasing the computational budget with respect to squished rectangular images. Finally, we formulate a set of best practices for transfer learning with greyscale inputs, potentially widely applicable in computer vision tasks ranging from footwear impression classification to medical imaging.

The interface between a ferromagnet (FM) or antiferromagnet (AFM) and a heavy metal (HM) results in an antisymmetric exchange interaction known as the interfacial Dzyaloshinskii-Moriya interaction (iDMI) which favors non-collinear spin configurations. The iDMI is responsible for stabilizing noncollinear spin textures such as skyrmions in materials with bulk inversion symmetry. Interfacial DMI values have been previously determined theoretically and experimentally for FM/HM interfaces, and, in this work, values are calculated for the metallic AFM MnPt and the insulating AFM NiO. The heavy metals considered are W, Re, and Au. The effects of the AFM and HM thicknesses are determined. The iDMI values of the MnPt heterolayers are comparable to those of the common FM materials, and those of NiO are lower.

A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model.

Networks are everywhere and their many types, including social networks, the Internet, food webs etc., have been studied for the last few decades. However, in real-world networks, it's hard to find examples that can be easily comparable, i.e. have the same density or even number of nodes and edges. We propose a flexible and extensible NetSim framework to understand how properties in different types of networks change with varying number of edges and vertices. Our approach enables to simulate three classical network models (random, small-world and scale-free) with easily adjustable model parameters and network size. To be able to compare different networks, for a single experimental setup we kept the number of edges and vertices fixed across the models. To understand how they change depending on the number of nodes and edges we ran over 30,000 simulations and analysed different network characteristics that cannot be derived analytically. Two of the main findings from the analysis are that the average shortest path does not change with the density of the scale-free network but changes for small-world and random networks; the apparent difference in mean betweenness centrality of the scale-free network compared with random and small-world networks., Comment: This paper has been accepted for the 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2018). The conference will be held in September, 2018. The original paper has 10 pages but the arXiv version has 11 pages due to slightly bigger fonts