Static Applications Research Articles

Systematic Approach to Prevent Code Vulnerabilities using CI/CD Pipelines

This paper discusses a systematic approach to integrating Static Application Security Testing (SAST), Software Composition Analysis (SCA), Code Coverage, and Code Quality Checks into Continuous Integration/Continuous Delivery (CI/CD) pipelines. Modern CI/CD pipelines accelerate software delivery but introduce significant security and quality challenges. By incorporating SAST and SCA for security testing, along with code coverage and quality checks, organizations can prevent code vulnerabilities and ensure the maintainability and reliability of their applications. This approach helps development teams shift security and quality controls left, catching issues early in the development lifecycle. Keywords — CI/CD, DevSecOps, Static Application Security Testing (SAST), Software Composition Analysis (SCA), Code Coverage, Code Quality, Vulnerability

Review of Angular-Selective Windows with Guest–Host Liquid Crystals for Static Window Applications

This review focuses on the development and advancements in angular-selective smart windows, with particular emphasis on static windows utilizing guest–host liquid crystal (GHLC) systems. Angular-selective windows are designed to adjust their transmittance based on the angle of incident light, offering enhanced energy efficiency and visual comfort in both architectural and automotive applications. By leveraging the anisotropic absorption properties of dichroic dyes, GHLC-based windows can selectively block oblique sunlight while preserving clear visibility from normal viewing angles. Various liquid crystal (LC) alignment configurations, including vertically aligned, homogeneously aligned, hybrid aligned, uniformly lying helix, and twisted aligned LC cells, have been investigated to optimize light control for different installation angles, such as for automotive windshields and building windows. These advancements have demonstrated significant improvements in energy conservation and occupant comfort by reducing cooling demands and regulating sunlight penetration. This review summarizes key findings from recent studies, addresses the limitations of current technologies, and outlines potential future directions for further advancements in smart window technology.

Affordable Real-Time PPP—Combining Low-Cost GNSS Receivers with Galileo HAS Corrections in Static, Pseudo-Kinematic, and UAV Experiments

The Galileo High Accuracy Service (HAS) is a free of charge Global Navigation Satellite System (GNSS) augmentation service provided by the European Union. It is designed to enable real-time Precise Point Positioning (PPP) with a target accuracy (at the 95% confidence level) of 20 cm and 40 cm in the horizontal and vertical components, respectively, to be achieved within 300 s. The performance of the service has been confirmed with geodetic-grade receivers. However, mass market applications require low-cost GNSS receivers connected to low-cost antennae. This paper focuses on the performance of the real-time static and kinematic positioning achieved with Galileo HAS and low-cost GNSS receivers. The study is limited to GPS+Galileo dual-frequency positioning, thus exploiting the full potential of Galileo HAS SL1. We demonstrate that the target accuracy of Galileo HAS SL1 is reached with both geodetic-grade and low-cost receivers in dual-frequency static and kinematic applications in open-sky conditions. Precision of a few centimeters is reached for static positioning, while kinematic positioning results in subdecimeter precision. Vertical accuracy is limited by missing phase center offset models for low-cost antennas. In general, the performance of low-cost hardware using Galileo HAS for real-time PPP is comparable to that of geodetic-grade hardware. Therefore, combining low-cost GNSS receivers with Galileo HAS is feasible and justified.

MRI recovery with self-calibrated denoisers without fully-sampled data.

Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe iteratively trains the denoiser on the image or images that are being reconstructed. We introduce two variations of our method: ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements and provides faster inference. Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. ReSiDe-S and ReSiDe-M outperform other methods in terms of peak signal-to-noise ratio and structural similarity index measure for Studies I and II, and in terms of expert scoring for Study III. We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.

Effective Acoustic Model-Based Beamforming Training for Static and Dynamic Hri Applications.

Human-robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human-robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods.

Effect of sawdust fillers loaded on Cucumis sativus fiber‐reinforced polymer composite: a novel composite for lightweight static application

AbstractThis research investigates the impact of sawdust fillers on Cucumis sativus fiber‐reinforced polymer composites through a conventional hand layup process. The objective is to develop a novel material suitable for static applications that is both lightweight and environmentally sustainable. A range of analytical techniques including X‐ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, mechanical testing, thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and energy‐dispersive X‐ray (EDX) analysis were employed to thoroughly characterize the resulting composite material. By integrating Cucumis sativus fibers and sawdust fillers into a polymer matrix, the study demonstrates the potential to create materials with improved mechanical properties due to addition of sawdust filler, including tensile strength (27.61 MPa), flexural strength (32.84 MPa), impact resistance (14.7 J cm−2) and hardness (42). These enhancements, averaging at 16.2%, are attributed to the addition of sawdust filler, which opens new avenues for environmentally conscious engineering solutions. XRD analysis reveals the composite's crystalline structure, indicating a crystallinity index of 64.5% and the orientation of crystalline planes. FTIR spectroscopy identifies chemical bonding and CO and CO functional groups present in the material with major peaks at 2123 and 2438 cm−1. TGA assesses the composite's thermal stability and decomposition behavior up to 380 °C. Additionally, SEM imaging elucidates the microstructural features and distribution of Cucumis sativus fibers and sawdust fillers within the epoxy matrix, while EDX analysis provides quantitative data on elemental composition. © 2024 Society of Chemical Industry.

SpQuant-SNN: ultra-low precision membrane potential with sparse activations unlock the potential of on-device spiking neural networks applications.

Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and energy efficiency. The binary spike-based information propagation enables efficient sparse computation in event-based and static computer vision applications. However, the weight precision and especially the membrane potential precision remain as high-precision values (e.g., 32 bits) in state-of-the-art SNN algorithms. Each neuron in an SNN stores the membrane potential over time and typically updates its value in every time step. Such frequent read/write operations of high-precision membrane potential incur storage and memory access overhead in SNNs, which undermines the SNNs' compatibility with resource-constrained hardware. To resolve this inefficiency, prior works have explored the time step reduction and low-precision representation of membrane potential at a limited scale and reported significant accuracy drops. Furthermore, while recent advances in on-device AI present pruning and quantization optimization with different architectures and datasets, simultaneous pruning with quantization is highly under-explored in SNNs. In this work, we present SpQuant-SNN, a fully-quantized spiking neural network with ultra-low precision weights, membrane potential, and high spatial-channel sparsity, enabling the end-to-end low precision with significantly reduced operations on SNN. First, we propose an integer-only quantization scheme for the membrane potential with a stacked surrogate gradient function, a simple-yet-effective method that enables the smooth learning process of quantized SNN training. Second, we implement spatial-channel pruning with membrane potential prior, toward reducing the layer-wise computational complexity, and floating-point operations (FLOPs) in SNNs. Finally, to further improve the accuracy of low-precision and sparse SNN, we propose a self-adaptive learnable potential threshold for SNN training. Equipped with high biological adaptiveness, minimal computations, and memory utilization, SpQuant-SNN achieves state-of-the-art performance across multiple SNN models for both event-based and static image datasets, including both image classification and object detection tasks. The proposed SpQuant-SNN achieved up to 13× memory reduction and >4.7× FLOPs reduction with < 1.8% accuracy degradation for both classification and object detection tasks, compared to the SOTA baseline.

Analytical Analysis of Buckling Of Layered Composite Columns

Abstract: Lightweight composite construction is of great interest in the design and manufacture of spacecraft and marine vehicles because of high specific stiffness and strength. In addition, sandwich construction offers enhanced corrosion resistance, noise suppression and reduction in life-cycle costs. There are several issues and questions related to the use of composite construction that require attention and answers. One of the important issues is the prediction of buckling loads, under either static or sudden load application. For the static case, closed form solutions are derived for a symmetric composite column under axial compression and for various boundary conditions. In this study, improved analytical models are developed to characterize interface behavior of layered composite column-type structures. The mechanics models of the bonded interface with and without adhesive layer are established, from which the interface fracture, delamination buckling, free vibration and stress analysis across the adhesive layer of corresponding interface are studied.

SAST Tools and Manual Testing to Improve the Methodology of Vulnerability Detection in Web Applications

Statically analyzing code during development is a common process of the development process, using Static Application Security Testing tools. SAST analyzes code without its execution and is also very fast compared to dynamic means and therefore focuses on a certain program part. However, the results of static analysis tools are not always accurate, either missing vulnerabilities or reporting false positives. This paper considers an evaluation of several SAST tools and an analysis of student code samples with known vulnerabilities, comparing manual analysis with the results of SAST tools. The results confirmed that SAST tools properly identify critical vulnerabilities and provide errors. A tool has identified ShiftLeft as the most efficient tool; however, its findings overlapped with the results of other tools for some applications. In addition, an analysis of student projects showed the most frequent vulnerabilities as Cross-site Scripting (XSS), NoSQL/SQL Injection, and Server-Side Request Forgery (SSRF) which make up more than 52% of the found vulnerabilities.

Accuracy and Uncertainty of Position Detection of Moving Objects by Resonator Arrays

Arrays of magnetically coupled resonators have recently been proposed as position sensors for both static and dynamic applications. Despite their promising characteristics, an accurate evaluation of their functioning in non-ideal conditions is fundamental and necessary to adapt them to industrial contexts. This article analyses the accuracy of the position measurement in resonator arrays under system parameter variations. Appropriate parameters for the evaluation of the sensor performance have been discussed and analysed considering non-ideal system components by exploiting the Monte Carlo method.

Android malware detection through centrality analysis of applications network

Android OS is a widely-used platform for mobile devices. However, with the increasing number of Android applications and ongoing advancements in application development, there is a need for flexible and scalable malware detection methods that can address the challenges posed by big data. Recently, researchers have developed methods based on complex network analysis that aim to reduce the complexity and enhance the scalability of malware detection. These methods have shown high accuracy in identifying Android malware. Our proposed method involves generating two weighted graphs that depict the relationships between applications in benign and malware states, respectively, by extracting the functions of each application. Network-analysis-based features are then extracted from the graphs and combined with static application features to distinguish malware applications from benign ones. Our approach demonstrated an increase in accuracy, achieving 99% and 98% accuracy on the DataMD and IntDroid datasets, respectively. We further demonstrate our proposed method’s superiority against state-of-the-art approaches.

Compressive behavior of thermoplastic polyurethane with an active agent foaming for 3D-printed customized comfort insoles

The primary objective of this study was to investigate the compressive behavior of 3D-printed specimens made of a thermoplastic polyurethane filament, specifically colorFabb varioShore TPU, which incorporates a foaming agent. The foaming technology makes it possible to manipulate 3D prints' properties by adjusting process parameters that affect the level of expansion, such as printing temperature, printing speed or flow ratio. This capability opens new possibilities for 3D-printed personalized foot orthotics or for static comfort-oriented applications by tailoring the stiffness of the print based on the application requirements. Since infill density and infill pattern also affect the compressive characteristics of 3D prints, this research took these two parameters into account as independent variables, alongside the printing temperature. Thus, the specimens with printing temperatures set at 190 °C, 220 °C, and 240 °C, gyroid and honeycomb patterns, and infill variations from 10 % to 35 % in 5 % increments were experimentally investigated in compression testing. The evaluation was conducted to determine the influence of these factors on the stiffness of prints at 10 % and 20 % strains, in accordance with established standard. The findings had an important practical value as they provide data for adjusting the variable stiffness based on peak plantar pressure measurement data when developing tailored foot orthoses (insoles), which was the second objective of the research. The most influential factor affecting the compressive strength was the printing temperature, followed by infill density and pattern type. The difference in compression modulus between 10 % and 20 % strains did not exhibit statistical significance. Moreover, the main effects plots indicated minimal variations in compression modulus effects at 220 °C and 240 °C, whereas an important difference was observed at 190 °C printing temperature, which was confirmed by scanning electron microscopy investigations showing foamed layers at 220 °C and unfoamed layers at 190 °C. Furthermore, interaction plots revealed no significant interaction effect among the studied variables. Additionally, a comparison of samples’ sizes and densities before and two months after testing indicated no observable modifications When applied to custom orthotics (five pairs of insoles were customized and tested), the experimental findings documented variations in the reduction and distribution of peak plantar pressure based on the infill variability.

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

Rapid hematocrit estimation using a fold-crease induced fast flowing paper sensor

Increased evaporative losses and flow obstructions can present substantial impediments to current paper analytical devices (µPADs) for efficient on-site testing of biological fluids. Strategic enhancements in wicking rates of paper may thereby counter these limitations and enable on-demand healthcare monitoring. Therefore, herein we have leveraged the features of paper fold-crease regions and developed a novel fast-flowing platform using laser printing to accelerate fluid flow through paper. A series of extensive experiments have been conducted to optimize the design and maximize wicking rates of µPADs for smaller liquid volumes, making it well-suited for analysing biofluids. The investigation delves into structural alterations within the creased regions, employing both static and dynamic force application strategies. A first-generation Washburn type model in excellent agreement with the experimental findings is developed, providing a comprehensive insight into the fundamental physics involved. Finally, the folded channels are utilized for a distance-based hematocrit sensor employing grade-1 filter paper at very low-cost, simplified fabrication, lesser sample volume and faster analysis. The findings of this work unveil a plethora of potentialities for employing paper and paper folds to develop affordable medical devices with advanced analytical functionalities, specifically tailored for the resource-constrained settings.

Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)

The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables’ importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems.

Mortality Prediction by Kinetic Parameters of Lactate and S-Adenosylhomocysteine in a Cohort of Critically Ill Patients.

Tissue hypoxia is associated with the development of organ dysfunction and death in critically ill patients commonly captured using blood lactate. The kinetic parameters of serial lactate evaluations are superior at predicting mortality compared with single values. S-adenosylhomocysteine (SAH), which is also associated with hypoxia, was recently established as a useful predictor of septic organ dysfunction and death. We evaluated the performance of kinetic SAH parameters for mortality prediction compared with lactate parameters in a cohort of critically ill patients. For lactate and SAH, maxima and means as well as the normalized area scores were calculated for two periods: the first 24 h and the total study period of up to five days following ICU admission. Their performance in predicting in-hospital mortality were compared in 99 patients. All evaluated parameters of lactate and SAH were significantly higher in non-survivors compared with survivors. In univariate analysis, the predictive power for mortality of SAH was higher compared with lactate in all forms of application. Multivariable models containing SAH parameters demonstrated higher predictive values for mortality than models based on lactate parameters. The optimal models for mortality prediction incorporated both lactate and SAH parameters. Compared with lactate, SAH displayed stronger predictive power for mortality in static and dynamic application in critically ill patients.

Materials and manufacturing technology for high reliability rigid-flex multilayer PCB for space 3D-electronics packaging

Rigid-flex Multilayer PCB Technology enables the elimination of several connectors and bulky harness for interconnecting functional electronic systems. This minimizes packaging complexity and reduces performance degradation at high frequencies with an added advantage of reduced weight and volume in high-reliability space electronic packages. Here, only thermo-mechanically stable adhesiveless all polyimide flexible laminate cores and no-flow glass-polyimide prepregs are employed. To overcome the manufacturing issues like registration, coverlay protrusion into the rigid portions, and residual copper deposition at the intersections of flexible and rigid portions; a novel manufacturing process technology is proposed here. The technology has been successfully devised after choosing the most appropriate flexible, rigid, and low-flow prepreg materials. Herein, for the realization of rigid-flex multilayer PCB technology with 2-layers of flex and 8-layers of rigid to meet the functional requirements of the board is presented. The critical registration issues associated with the dissimilar rigid and flexible substrates are successfully addressed to achieve best-fit registration and obtain minimum annular rings of 50 μm in all the inner layers for high reliability. Experimental results manifest that incorporating rigid-flex multilayer PCB technology dramatically reduces weight (>30%) and volume (∼40%) to high-reliability space electronics 3D packaging for static and dynamic applications.

Co-rotational 3D beam element using quaternion algebra to account for large rotations: Formulation theory and static applications

This paper presents a co-rotational beam element based on quaternion algebra as a means of parameterizing large rotations. This co-rotational framework is based on a decomposition of beam kinematics into a rigid element frame, which follows the element and its pure deformation. Once the decomposition between rigid body motion and deformation is obtained, the principle of virtual work allows calculating the element response projected onto large displacements and rotations. This 3D co-rotational element lies within the framework of incremental formulations. The special feature of this formulation pertains to the decomposition of kinematics to extract the rigid body motion and pure deformations through use of quaternion algebra by solving an internal nonlinear kinematic equation. Thus, from the 2 quaternions defining global rotations and the 2 displacement vectors at the ends of the beam, this method is able to extract: 1 quaternion and 1 displacement vector parameterizing the rigid body motion, plus 2 other quaternions defining the internal rotations and beam elongation. The proposed formulation based on quaternion algebra therefore constitutes an alternative to the literature’s treatment of large-rotation kinematics using a quaternion algebra. The operators are also linearized in order to obtain the algorithmic stiffness operator. Eleven distinct static numerical applications are presented, along with comparisons from the literature in the aim of demonstrating the efficiency of this proposed element.

Dynamic Co-Embedding Model for Temporal Attributed Networks.

In this article, we study the problem of embedding temporal attributed networks, with the goal of which is to learn dynamic low-dimensional representations over time for temporal attributed networks. Existing temporal network embedding methods only learn the representations for nodes, which are unable to capture the dynamic affinities between nodes and attributes. Moreover, existing co-embedding methods that learn the static embeddings of both nodes and attributes cannot be naturally utilized to obtain their dynamic embeddings for temporal attributed networks. To address these issues, we propose the dynamic co-embedding model for temporal attributed networks (DCTANs) based on the dynamic stochastic state-space framework. Our model captures the dynamics of a temporal attributed network by modeling the abstract belief states representing the condition of the nodes and attributes of current time step, and predicting the transitions between temporal abstract states of two successive time steps. Our model is able to learn embeddings for both nodes and attributes based on their belief states at each time step of the temporal attributed network, while the state transition tendency for predicting the future network can be tracked and the affinities between nodes and attributes can be preserved. Experimental results on real-world networks demonstrate that our model achieves substantial performance gains in several static and dynamic graph mining applications compared with the state-of-the-art static and dynamic models.

A Review of Standard in Inductive Power Transfer

Contactless charging is evolving fast in many domains as for example on the electrical vehicles (EV) domain. The principle consists of transferring energy between two systems without having any electrical connection between them. This power transfer can be realized by emitting a magnetic field and is called Inductive Power Transfer (IPT) for static application and DIPT for dynamic application. In the automotive domain, the application of contactless charging to supply power to the vehicle while driving is one of the solutions envisaged to solve the limited autonomy range of EV and reduced its cost but the system must comply with existing standards (e.g., allocated frequency range and radiated electromagnetic emissions), as summarized in this paper. We also show that we can, through this basic hypothesis, give a concrete meaning to “dark energy” and “dark matter”.