This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles.
Amidst the rapid global expansion of smart grids, ensuring the safety and reliability of power transmission systems has become paramount. Insulators are critical components of high-voltage transmission lines, providing both electrical insulation and mechanical support. However, their exposure to electrical, mechanical, and environmental stressors renders them vulnerable point within the system. Defective insulators are a major cause of failures in power transmission systems. Consequently, the early and accurate detection of these defects is pivotal for maintaining the integrity and reliability of the power grid. To address this challenge, this study proposes InsDD-YOLO, a novel object detection architecture enhanced from the YOLOv13 framework. The model incorporates a suite of strategic enhancements, including an improved DSConv (IDSConv) module for robust feature extraction, a streamlined Neck architecture augmented with a feature stream from a shallower layer (B2) to improve small-target detection, and a direct Head connection mechanism to maximize the preservation of fine-grained details. Experimental results demonstrate that InsDD-YOLO achieves superior performance, reaching an mAP0.5 of 90.1% and an mAP<inline-formula> <tex-math notation="LaTeX">${}_{0.5:0.95}$ </tex-math></inline-formula> of 46.4%, outperforming the baseline YOLOv13 model by a significant 5.0% in mAP0.5. With an inference time of just 5.4 ms, the proposed model not only establishes a new benchmark for accuracy but also demonstrates an effective trade-off between performance and speed, underscoring its significant potential for deployment in real-time, automated power grid monitoring systems.
The cohesion of an object-oriented class refers to the relatedness of its methods and attributes. Constructors, destructors, and access methods are special types of methods featuring unique characteristics that can artificially affect class cohesion quantification. Methods within a class can also directly or transitively invoke each other, representing another cohesion aspect not considered by most existing cohesion measures. The impact of considering special methods (SPs) and transitive relations (TRs) in cohesion measurement on the abilities of the measures to predict inheritance reusability has yet to be investigated. In this paper, we empirically explored this effect. We applied a statistical technique to test the significance of the cohesion value changes across seven scenarios of ignoring or considering SPs and TRs. In addition, we applied a machine learning-based technique to build inheritance reusability prediction models using each of the considered measures and scenarios, evaluated the classification performance of the prediction models, and statistically compared the inheritance reusability prediction results. The results show that for most of the considered measures, the ignorance/consideration of SPs and TRs changed the cohesion values and the corresponding prediction significantly. Based on the study findings, when building inheritance reusability prediction models, software engineers are advised to 1) combine cohesion with other quality factors; 2) exclude the TRs from cohesion quantification; and 3) decide whether to consider or ignore SPs in cohesion quantification based on the selected measure(s) to be used in the prediction model, as this decision differs from one measure to another.
Abstract Potential evaluation to assist demand response decisions has garnered significant attention with the development of new power systems. However, existing data‐driven methods are challenging to properly exploit multivariate features and the process of response potential evaluation is unclear. Therefore, the authors propose an evaluation method that fuses expert features with multi‐image inputs and analyses the model evaluation process based on gradient. First, typical load profiles are extracted by the proposed procedure. Next, features derived from expert knowledge are calculated from the perspectives of adjustability, regularity, and sensitivity of electricity usage. Additionally, the typical load profile's recurrence plot, Markov leapfrog field, and Gramian angle field are created and incorporated into the colourful image as inputs. Then, the evaluation results are obtained by a two‐stream neural network fusing multivariate features. In the experiments, the proposed method is validated and discussed by comparing with many existing methods using London household users' data under the time‐of‐use price, providing new insights for demand response potential evaluation.
David J. Ibberson, James Kirkman, John J. L. Morton
et al.
Quantum processing units will be modules of larger information processing systems containing also digital and analog electronics modules. Silicon-based quantum computing offers the enticing opportunity to manufacture all the modules using the same technology platform. Here, we present a cryogenic multi-module assembly for multiplexed readout of silicon quantum devices where all modules have been fabricated using the same fully-depleted silicon-on-insulator (FDSOI) CMOS process. The assembly is constituted by three chiplets: (i) a low-noise amplifier (LNA), (ii) a single-pole eight-throw switch (SP8T), and (iii) a silicon quantum dot (QD) array. We integrate the chiplets into modules and show respectively, (i) a peak gain over 35dB with a 3dB bandwidth from 709MHz to 827MHz and an average noise temperature of 6.2K (minimum 4.2K), (ii) an insertion loss smaller than 1.1dB and a noise temperature less than 1.1K over the 0-2GHz range, and (iii) single-electron box (SEB) charge sensors. Finally, we combine all modules into a single demonstration showing time-domain radio-frequency multiplexing of two SEBs paving the way to an all-silicon quantum computing system.
Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements-encapsulated by the term nanophotonics-has led to new fundamental science and innovative technologies across the entire electromagnetic spectrum, with substantial emphasis on the microwave to visible regimes. In this review, we pay special attention to the impact and potential of nanophotonics in a relatively exotic yet technologically disruptive regime: high-energy particles such as X-ray photons and free electrons-where nanostructures and atomic design open the doors to unprecedented technologies in quantum science and versatile X-ray sources and optics. As the practical generation of X-rays is intrinsically linked to the existence of energetic free or quasi-free-electrons, our review will also capture related phenomena and technologies that combine free electrons with nanophotonics, including free-electron-driven nanophotonics at other photon energies. In particular, we delve into the demonstration and study of quantum recoil in the X-ray regime, the study of nanomaterial design and free-electron wave shaping as means to enhance and control X-ray radiation, examine the free-electron generation enabled by nanophotonics, and analyze the high-harmonic generation by quasi-free electrons. We also discuss applications of quantum nanophotonics for X-rays and free electrons, including nanostructure waveguides for X-rays, photon pair enhanced X-ray imaging, mirrors, and lenses for X-rays, among others.
Chaudry Sajed Saraj, Subhash C. Singh, Gopal Verma
et al.
Transition–metal-doped electrocatalysts are considered as low-cost alternatives of Pt and RuO2 electrocatalysts for large scale electrochemical generations of hydrogen and oxygen, respectively. Although, chemical synthesis, typically adopted to produce these electrocatalysts, is scalable but hazardous by-products and chemical wastes create growing environmental concerns. Here, we developed a single step, single pot, and environmentally friendly physical approach of electric field-assisted pulsed laser ablation in liquid for the synthesis of colloidal solution of pure CuMoO4 (CMO) electrocatalysts. The entire process took few minutes and did not involve or generate any chemical. A pulsed picosecond laser was used to ablate MoS2 target at the solid-liquid interface to generate spatially confined plasma plume. Two parallel electrodes (copper sheets) were mounted around the plasma plume to modulate the plasma parameters, control the reactions at the plasma-liquid interface, and simultaneously inject copper ions from the electrode to the laser-produced plasma (LPP) for the generation of CMO. nanoparticles. Surprisingly, we observed that by varying the applied electric field, we can efficiently control the size, shape, crystallinity, morphology, and composition of as produced CMO nanocomposites and enhance their hydrogen evolution reaction (HER) performance. The characterization results proves that the introduction of applied electric field during the laser ablation process significantly change the morphology of as-prepared nanomaterials, and the shape of these nanomaterials were spherical, spindle and cuboid for MoS2, CuO and CMO respectively. Among all the fabricated electrocatalysts, CMO-60 is the best HER performer in alkaline medium, while MoS2 and CuO nanoparticles were the worse. For CMO-60 sample, only 440 mV overpotential required to reach the current density of 10 mA/cm2 and as well as posess good stability. We found that electrocatalysts produced at a higher electric field have higher contents of copper and oxygen leading to a superior HER activity. The developed approach can be applied for the synthesis of other electrocatalysts for a range of chemical reactions.
Materials of engineering and construction. Mechanics of materials, Industrial electrochemistry
The study focuses on addressing the image defocusing issue caused by motion errors in highly squinted Synthetic Aperture Radar (SAR). The traditional auto-focusing algorithm, Phase Gradient Autofocus (PGA), is not effective in this mode due to difficulties in estimating the phase gradient accurately from strong point targets. Two main reasons contribute to this problem. Firstly, the direction of the energy-distributed lines in the Point Spread Function (PSF) does not align with the image’s azimuth direction in highly squinted mode. Secondly, the wavenumber spectrum of high squint SAR images obtained using the Back-Projection Algorithm (BPA) varies spatially, causing aliasing in the azimuth spectrum of all targets. In this paper, a new auto-focusing method is proposed for highly squinted SAR imaging. The modifications to the BP imaging grids have been implemented to address the first problem, while a novel wavenumber spectrum shifting and truncation method is proposed to accurately extract the phase gradient and tackle the spatial variation issue. The feasibility of the proposed algorithm is verified through simulations with point targets and processing of real data. The evaluation of the image shows an average improvement of four times in PSLR (Peak-Sidelobe-to-Noise Ratio).
Michela Prunella, Roberto Maria Scardigno, Domenico Buongiorno
et al.
Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.
Since tungsten (W) was considered as the most promising plasma facing materials (PFMs) in fusion reactors, there has been extensive research on the physical performance of W-PFMs. It is found that under the extreme conditions in a fusion reactor, W-PFMs should be in a nonequilibrium state of high electronic temperature and low ionic temperature. This leads to the possibility of non-thermal phase transitions, where the crystal structure of the tungsten material may change from body-centered cubic (bcc) phase to hexagonal close-packed (hcp) phase or face-centered cubic (fcc) phase. Consequently, it is necessary to investigate the relevant physical properties of hcp-W and fcc-W under the electron-excited state. In this work, the fundamental physical properties, including atomic structures, electronic structures, elastic constants, and vacancy formation energies, of bcc-W, hcp-W and fcc-W, were theoretically calculated at various electronic temperatures. The mechanical stability of these three phases was also systematically analyzed under varying electronic temperatures. The results of this research are expected to provide a certain guidance in the optimization of W-PFMs in future fusion reactors.
The microstructures and phase formations of Ti20Zr15Hf15Ni35Cu15 high entropy shape memory alloy (HESMA) under different aging conditions were investigated in this study. At aging temperatures below 500 °C, a large amount of the H-phase formed, and the martensitic transformation temperatures were suppressed due to the strain field around the H-phase. Aging treatment at 600 °C caused a eutectoid reaction, which yielded a lamellar structure composed of (Zr,Hf)7Cu10 and Ti2Cu phases. When the aging treatment was increased to 700 °C, the lamellar structure was no longer observed, but (Zr,Hf)7Cu10 and newly-formed Ti2Ni phases formed around the original Ti2Ni phase. Experimental results demonstrated that the H-phase precipitation, eutectoid decomposition, and (Zr,Hf)7Cu10 formation occurred at different aging temperatures. These results could be utilized to adjust the martensitic transformation temperatures and design microstructures, providing a new strategy for developing TiZrHfNiCu HESMAs.
Materials of engineering and construction. Mechanics of materials
Vegard Steinsland, Lars Michael Kristensen, Shujun Zhang
We apply Coloured Petri Nets (CPNs) and the CPN Tools to develop a formal model of an embedded system consisting of a power converter and an associated controller. Matlab/Simulink is the de-facto tool for embedded control and system design, but it relies on informal semantics and has limited support for transparent and integrated specification and validation of both the power converter electronics, controller (hardware), and the control logic (software). The contribution of this paper is to develop a timed hierarchical CPN model that mitigates the shortcomings of Simulink by relying on a Petri net formalisation. We demonstrate the application of our approach by developing a fully integrated model of a buck power converter with controller in CPN Tools. Furthermore, we perform time-domain simulation to verify the capability of the controller to serve the control objectives. To validate the developed CPN model, we compare the simulation results obtained in an open-loop configuration with a corresponding implementation in Simulink. The experimental results show correspondence between the CPN model and the Simulink model. As our CPN model reflects the fully integrated system, we are able to compare CPN simulation results to measurements obtained with a corresponding implementation in real hardware/software and compare closed-loop with open-loop configuration. The results show alignment for the steady state while further refinement of the control algorithm and validation is required.
Abstract Some of the variants detected by high-throughput sequencing (HTS) are often not reproducible. To minimize the technical-induced artifacts, secondary experimental validation is required but this step is unnecessarily slow and expensive. Thus, developing a rapid and easy to use visualization tool is necessary to systematically review the statuses of sequence read alignments. Here, we developed a high-performance alignment capturing tool, CaReAl, for visualizing the read-alignment status of nucleotide sequences and associated genome features. CaReAl is optimized for the systematic exploration of regions of interest by visualizing full-depth read-alignment statuses in a set of PNG files. CaReAl was 7.5 times faster than IGV ‘snapshot’, the only stand-alone tool which provides an automated snapshot of sequence reads. This rapid user-programmable capturing tool is useful for obtaining read-level data for evaluating variant calls and detecting technical biases. The multithreading and sequential wide-genome-range-capturing functionalities of CaReAl aid the efficient manual review and evaluation of genome sequence alignments and variant calls. CaReAl is a rapid and convenient tool for capturing aligned reads in BAM. CaReAl facilitates the acquisition of highly curated data for obtaining reliable analytic results.
Computer engineering. Computer hardware, Information technology
In multi-view learning, massive literature is devoted to exploring the intrinsic structure between cross-views. It is well known that canonical correlation analysis (CCA) is a conventional multi-view learning method, which considers the correlation between two views. However, it fails to utilize class information and is difficult to suit different issues to extract discriminative features. In this paper, we propose a novel cross-view discriminative feature learning method called dynamic discriminative canonical correlation analysis, which captures class information to yield discriminative features. More specifically, we develop an adaptive weight scheme of cross-view within-class and between-class scatters to make full use of distribution class information. In addition, an iterative algorithm with Cauchy inequalities and the Lagrange multiplier is proposed to handle the non-smooth objective function. Our method is applied to face recognition and multi-linguistic text classification tasks. Extensive experimental results reveal that the adaptive weight scheme plays a beneficial role and our method is an effective feature learning.
Abstract Abdominal aortic aneurysm (AAA) refers to the enlargement of the lower artery of the abdominal aorta, and identification of an early detection tool is urgently needed for diagnosis. In the current study, an interdigitated electrode (IDE) sensing surface was used to identify miRNA-335-5p, which reflects the formation of AAAs. The uniformity of the silica material was observed by 3D profilometry, and the chemically modified highly conductive surface improved the detection via the I-V mode. The targeted miRNA-335-5p was detected in a dose-dependent manner and based on linear regression and 3σ analyses, the sensitivity was determined to be 1 fM with a biotinylated probe. The high specificity was shown by discriminating the target sequence from noncomplementary and single- and triple-mismatched sequences. These outputs demonstrated the high-performance detection of miRNA-335-5p with good reproducibility for determination of the severity of AAA.
Materials of engineering and construction. Mechanics of materials
Hsien-Chin Chiu, Chia-Hao Liu, Yi-Sheng Chang
et al.
The surface morphology optimization of ohmic contacts and the Mg out-diffusion suppression of normally off p-GaN gate high-electron-mobility transistors (HEMTs) continue to be challenges in the power electronics industry in terms of the high-frequency switching efficiency. In this study, better current density and reliable dynamic behaviors of p-GaN gate HEMTs were obtained simultaneously by adopting low-temperature microwave annealing (MWA) for the first time. Moreover, HEMTs fabricated using MWA have a higher ION/IOF ratio and lower gate leakage current than the HEMTs fabricated using rapid thermal annealing. Due to the local heating effect, a direct path for electron flow can be formed between the two-dimensional electron gas and the ohmic metals with low bulges surface. Moreover, the Mg out-diffusion of p-GaN gate layer was also suppressed to maintain good current density and low interface traps.
The current integrated circuit (IC) technology based on conventional MOS-FET (metal-oxide-semiconductor field-effect transistor) is approaching the limit of miniaturization with increasing demand on energy. Several analog circuit applications based on graphene FETs have been demonstrated with less components comparing to the conventional technology. However, low on/off current ratio caused by the semimetal nature of graphene has severely hindered its practical applications. Here we report a graphene/MoTe2 van der Waals (vdW) vertical transistor with V-shaped ambipolar field effect transfer characteristics to overcome this challenge. Investigations on temperature dependence of transport properties reveal that gate tunable asymmetric barriers of the devices are account for the ambipolar behaviors. Furthermore, to demonstrate the analog circuit applications of such vdW vertical transistors, we successfully realized output polarity controllable (OPC) amplifier and frequency doubler. These results enable vdW heterojunction based electronic devices to open up new possibilities for wide perspective in telecommunication field.
Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep Neural Networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid-classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a 5-layer phase-only D2NN jointly-optimized with a single fully-connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10x10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design.
Douglas Natelson, Charlotte I. Evans, Pavlo Zolotavin
In metal nanostructures under illumination, multiple different processes can drive current flow, and in an open- circuit configuration some of these processes lead to the production of open-circuit photovoltages. Structures that have plasmonic resonances at the illumination wavelength can have enhanced photovoltage response, due to both increased interactions with the incident radiation field, and processes made possible through the dynamics of the plasmon excitations themselves. Here we review photovoltage response driven by thermoelectric effects in continuous metal nanowires and photovoltage response driven by hot electron production and tunneling. We discuss the prospects for enhancing and quantifying hot electron generation and response via the combination of local plasmonic resonances and propagating surface plasmon polaritons.