Search Results (43)

Electroencephalography (EEG) is an important noninvasive diagnostic tool for detecting neurodegenerative disorders. In this context, EEG-based deep learning models show promise due to their ability to capture nonlinear brain dynamics but often suffer from poor generalizability caused by high inter-subject variability. Self-supervised learning (SSL) offers a promising solution by enabling models to learn robust representations from large unlabeled datasets. This study introduces a double-masking representation learning framework for EEG analysis. Using data aggregated from eight multi-center datasets (3156 subjects; 439 h of EEG recordings), a hybrid convolutional-transformer model (TransformEEG) is pretrained to enhance generalization in neurodegenerative disease classification, focusing on Parkinson’s and Alzheimer’s diseases. This approach combines phase-swap data augmentation, designed to facilitate the learning of EEG phase-amplitude coupling, with a double-masking function that operates at both the signal and the transformer’s token levels. The pretrained model was evaluated against a fully supervised baseline using Monte Carlo cross-validation with 100 splits across three public pathology detection datasets. Pretraining led to consistent improvements in both median balanced accuracy and Inter-Quartile Range (IQR) across all fine-tuning datasets. Compared to the fully supervised baseline, the proposed approach increases median balanced accuracy by 2.8% for Parkinson’s disease detection and by 4.2% for Alzheimer’s disease detection, while also reducing performance variability across 100 Monte Carlo splits. These results demonstrate that SSL can enhance EEG deep learning performance, though achieving robust generalization in clinical applications remains an open research challenge. Full article

The pure states of two-qubit quantum systems are described by a four-dimensional vector of complex numbers, and unitary operators transferring a two-qubit quantum system from one state to another have the form of a $4\times 4$ matrix with complex elements. This fact brings to mind the idea of studying the spaces of four-dimensional numbers with complex components. Moreover, the results obtained by the authors for four-dimensional numbers with real components inspire some optimism. In this paper we construct four-dimensional spaces of complex numbers by analogy with four-dimensional spaces of real numbers. Each four-dimensional number is mapped to a matrix formed from its components and it is proved that the constructed mapping is a bijection and a homomorphism. In the space of four-dimensional numbers of the eight basis elements, half are real and half are imaginary. The presence of such symmetry distinguishes these spaces from the space of quaternions, in which one basis element is real and the rest are imaginary. The symmetry of the basis numbers makes these spaces a natural generalization of one-dimensional and two-dimensional (complex) algebra. The conditions under which the corresponding matrices are gates for two-qubit quantum systems are defined. The notion of a unitary state of a two-qubit quantum system is introduced, to which various gates from commutative groups of gates correspond. It is shown that any gate of a unitary state transforms a unitary state into a unitary state and a non-unitary state into a non-unitary state. Almost all gates used in the construction of quantum circuits, in particular H, SWAP, CX, CY, and CZ, have the same properties. The problem of searching for a gate that transfers a quantum system from one unitary state to another unitary state has been solved. Thus, with the help of four-dimensional spaces of complex numbers it was possible to construct whole classes of two-qubit gates, which opens new possibilities for the construction of quantum algorithms. The results obtained have important theoretical and practical implications for quantum computing. Full article

As the world is technologically advancing, the integration of FSO communication in non-terrestrial platforms is transforming the landscape of global connectivity. By enabling high-data-rate inter-satellite links, secure UAV–ground channels, and efficient HAPS backhaul, FSO technology is paving the way for sustainable 6G non-terrestrial networks. However, the stringent requirement for precise line-of-sight (LoS) alignment between the optical transmitter and receivers poses a hindrance in practical deployment. As non-terrestrial missions require continuous movement across the mission area, the platform is subject to vibrations, dynamic motion, and environmental disturbances. This makes maintaining the LoS between the transceivers difficult. While fine-pointing mechanisms such as fast steering mirrors and adaptive optics are effective for microradian angular corrections, they rely heavily on an initial coarse alignment to maintain the LoS. Coarse pointing modules or gimbals serve as the primary mechanical interface for steering and stabilizing the optical beam over wide angular ranges. This survey presents a comprehensive analysis of coarse pointing and gimbal modules that are being used in FSO communication systems for non-terrestrial platforms. The paper classifies gimbal architectures based on actuation type, degrees of freedom, and stabilization strategies. Key design trade-offs are examined, including angular precision, mechanical inertia, bandwidth, and power consumption, which directly impact system responsiveness and tracking accuracy. This paper also highlights emerging trends such as AI-driven pointing prediction and lightweight gimbal design for SWap-constrained platforms. The final part of the paper discusses open challenges and research directions in developing scalable and resilient coarse pointing systems for aerial FSO networks. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

In a vast variety of prokaryotes such as Escherichia coli and Streptomyces lividans, the DNA degradation (Dnd) CDE protein complex (consisting of DndC, DndD, and DndE), together with the DndA/IscS protein and the DndFGH complex, function as a defense barrier to prevent the invasion of non-self-DNA. The DndCDE complex introduces phosphorothioation (PT) modifications into DNA, and the DndFGH complex specifically cleaves non-PT DNA and, thus, restricts horizontal gene transfer and phage invasion. Despite the central importance of the DndCDE complex in DNA PT modification, which catalyzes the oxygen–sulfur swap on DNA, our understanding of this key complex remains poor. Here, we employed protein structure prediction to provide a reasonably reliable prediction of the structure of the DndCDE complex and a 23 bp DNA-DndCDE complex. We found that among the three proteins in the DndCDE complex, DndC, especially its “specificity loop”, plays a key role in recognizing the consensus PT modification sequence. In addition, the DndD protein is found to possess a highly conserved structural surface on its globular domain, presumably mediating the dimerization of DndD as well as the DndCDE complex. Furthermore, our normal mode analysis showed that there exists a dynamic transition between a closed and an open state for the DndCDE complex, facilitating its association and release of DNA. Our conclusions were corroborated by biochemical assays using purified proteins. On the whole, we provide molecular insights into the assembly and DNA-recognition mechanism of a central protein complex involved in DNA phosphorothioation. Full article

Collision models have attracted significant attention in recent years due to their versatility to simulate open quantum systems in different dynamical regimes. They have been used to study various interesting phenomena such as the dynamical emergence of non-Markovian memory effects and the spontaneous establishment of synchronization in open quantum systems. In such models, the repeated pairwise interactions between the system and the environment and also the possible coupling between different environmental units are typically modeled using the coherent partial SWAP (PSWAP) operation as it is known to be a universal homogenizer. In this study, we investigate the dynamical behavior of incoherent collision models, where the interactions between different units are modeled by the incoherent controlled SWAP (CSWAP) operation, which is also a universal homogenizer. Even though the asymptotic dynamics of the open system in cases of both coherent and incoherent swap interactions appear to be identical, its transient dynamics turns out to be significantly different. Here, we present a comparative analysis of the consequences of having coherent or incoherent couplings in collision models, namely, PSWAP or CSWAP interactions, respectively, for the emergence of memory effects for a single-qubit system and for the onset synchronization between a pair of qubits, both of which are strictly determined by the transient dynamics of the open system. Full article

Wireless sensor networks deployed in energy-constrained environments face critical challenges relating to sustainability and protection. This paper introduces an innovative blockchain-powered safe energy-swapping protocol that enables sensor nodes to voluntarily and securely trade excess energy, optimizing usage and prolonging lifespan. Unlike traditional centralized management schemes, the proposed approach leverages blockchain technology to generate an open, immutable ledger for transactions, guaranteeing integrity, visibility, and resistance to manipulation. Employing smart contracts and a lightweight Proof-of-Stake consensus mechanism, computational and power costs are minimized, making it suitable for WSNs with limited assets. The system is built using NS-3 to simulate node behavior, energy usage, and network dynamics, while Python manages the blockchain architecture, cryptographic security, and trading algorithms. Sensor nodes checked their power levels and broadcast requests when energy fell under a predefined threshold. Neighboring nodes with surplus power responded with offers, and intelligent contracts facilitated secure exchanges recorded on the blockchain. The Proof-of-Stake-based consensus process ensured efficient and secure validation of transactions without the energy-intensive need for Proof-of-Work schemes. The simulation results indicated that the proposed approach reduces wastage and significantly boosts network resilience by allowing nodes to remain operational longer. A 20% increase in lifespan is observed compared to traditional methods while maintaining low communication overhead and ensuring secure, tamper-proof trading of energy. This solution provides a scalable, safe, and energy-efficient answer for next-generation WSNs, especially in applications like smart cities, precision agriculture, and environmental monitoring, where autonomy of energy is paramount. Full article

The proliferation of Internet of Things (IoT) devices and big data has catalyzed the emergence of data markets. Regulatory and technological frameworks such as International Data Spaces (IDS) have been developed to facilitate secure data exchange while integrating security and data sovereignty aspects required by laws and regulations, such as the GDPR and NIS2. Recently, novel attack vectors have taken a toll on many enterprises, causing significant damage despite the deployed security mechanisms. Hence, it is reasonable to assume that the IDS may be just as susceptible. In this paper, we conduct a STRIDE threat analysis on IDS to assess its susceptibility to traditional and emerging cybersecurity threats. Specifically, we evaluate novel threats such as Man-in-the-Middle (MitM) attacks, compromised end-user devices, SIM swapping, and potential backdoors in commonly used open-source software. Our analysis identifies multiple vulnerabilities, particularly at the trust boundary (TB) between users and the IDS system. These include the traditionally troublesome Denial of Service (DoS) attacks, key management weaknesses, and the mentioned novel threats. We discuss the hacking techniques, tools, and associated risks to the IDS framework, followed by targeted mitigation strategies and recommendations. This paper provides a framework for performing a STRIDE-based threat analysis of the IDS. Using the proposed methodology, we identified the most potent threats and suggested solutions, thus contributing to the development of a safer and more resilient data space architecture. Full article

To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations. Full article

A derivation of a tight-binding model from Schrödinger formalism for various topologies of position-based semiconductor qubits is presented in the case of static and time-dependent electric fields. The simplistic tight-binding model enables the description of single-electron devices at a large integration scale. The case of two electrostatically Wannier qubits (also known as position-based qubits) in a Schrödinger model is presented with omission of spin degrees of freedom. The concept of programmable quantum matter can be implemented in the chain of coupled semiconductor quantum dots. Highly integrated and developed cryogenic CMOS nanostructures can be mapped to coupled quantum dots, the connectivity of which can be controlled by a voltage applied across the transistor gates as well as using an external magnetic field. Using the anti-correlation principle arising from the Coulomb repulsion interaction between electrons, one can implement classical and quantum inverters (Classical/Quantum Swap Gate) and many other logical gates. The anti-correlation will be weakened due to the fact that the quantumness of the physical process brings about the coexistence of correlation and anti-correlation at the same time. One of the central results presented in this work relies on the appearance of dissipation-like processes and effective potential renormalization building effective barriers in both semiconductors and in superconductors between not bended nanowire regions both in classical and in quantum regimes. The presence of non-straight wire regions is also expressed by the geometrical dissipative quantum Aharonov–Bohm effect in superconductors/semiconductors when one obtains a complex value vector potential-like field. The existence of a Coulomb interaction provides a base for the physical description of an electrostatic Q-Swap gate with any topology using open-loop nanowires, with programmable functionality. We observe strong localization of the wavepacket due to nanowire bending. Therefore, it is not always necessary to build a barrier between two nanowires to obtain two quantum dot systems. On the other hand, the results can be mapped to the problem of an electron in curved space, so they can be expressed with a programmable position-dependent metric embedded in Schrödinger’s equation. The semiconductor quantum dot system is capable of mimicking curved space, providing a bridge between fundamental and applied science in the implementation of single-electron devices. Full article

Background: The Chear open-set performance test (COPT), which uses a carrier phrase followed by a monosyllabic test word, is intended for clinical assessment of speech recognition, evaluation of hearing-device performance, and the fine-tuning of hearing devices for speakers of British English. This paper assesses practice effects, test–retest reliability, and the variability across lists of the COPT. Method: In experiment 1, 16 normal-hearing participants were tested using an initial version of the COPT, at three speech-to-noise ratios (SNRs). Experiment 2 used revised COPT lists, with items swapped between lists to reduce differences in difficulty across lists. In experiment 3, test–retest repeatability was assessed for stimuli presented in quiet, using 15 participants with sensorineural hearing loss. Results: After administration of a single practice list, no practice effects were evident. The critical difference between scores for two lists was about 2 words (out of 15) or 5 phonemes (out of 50). The mean estimated SNR required for 74% words correct was −0.56 dB, with a standard deviation across lists of 0.16 dB. For the participants with hearing loss tested in quiet, the critical difference between scores for two lists was about 3 words (out of 15) or 6 phonemes (out of 50). Full article

The endothelin-1 (ET1) peptide has a pathological role in the activation of proliferation, survival and invasiveness pathways in different cancers. ET1’s effects rely on its activation by the endothelin-converting enzyme-1 (ECE1), which is expressed as four isoforms, differing only in their cytoplasmic N-terminuses. We already demonstrated in colorectal cancer, glioblastoma, and preliminarily lung cancer, that the isoform ECE1c heightens aggressiveness by promoting cancer stem cell traits. This is achieved through a non-canonical ET1-independent mechanism of enhancement of ECE1c’s stability upon CK2-dependent phosphorylation at S18 and S20. Here, a K6 residue is presumably responsible for ECE1c ubiquitination as its mutation to R impairs proteasomal degradation. However, how phosphorylation enhances ECE1c’s stability and how this translates into aggressiveness are still open questions. In this brief report, by swapping residues to either phospho-mimetic or phospho-resistant amino acids, we propose that the N-terminus may also be phosphorylated at Y5 and/or T9 by an unknown kinase(s). In addition, N-terminus phosphorylation may lead to a blockage of K6 ubiquitination, increasing ECE1c’s stability and presumably activating the Wnt/β-catenin signaling pathway. Thus, a novel CK2/ECE1c partnership may be emerging to promote aggressiveness and thus become a biomarker of poor prognosis and a potential therapeutic target for several cancers. Full article

Null models are crucial tools for investigating network topological structures. However, research on null models for higher-order networks is still relatively scarce. In this study, we introduce an innovative method to construct null models for hypergraphs, namely the hyperedge swapping-based method. By preserving certain network properties while altering others, we generate six hyper-null models with various orders and analyze their interrelationships. To validate our approach, we first employ hypergraph entropy to assess the randomness of these null models across four datasets. Furthermore, we examine the differences in important statistical properties between the various null models and the original networks. Lastly, we investigate the impact of hypergraph randomness on network dynamics using the proposed hyper-null models, focusing on dismantling and epidemic contagion. The findings show that our proposed hyper-null models are applicable to various scenarios. By introducing a comprehensive framework for generating and analyzing hyper-null models, this research opens up avenues for further exploration of the intricacies of network structures and their real-world implications. Full article

Heavy-duty vehicles are a major contributor to CO₂ emissions in the transportation sector, and it is necessary to develop clean and green technologies to replace diesel trucks. Electric trucks have not reached a breakthrough in the German market. In addition to technology development, customer acceptance of new technologies is a critical factor in the success of sustainable transportation policies. This study aims to fill this knowledge gap by investigating the perceptions regarding electric trucks and providing insights into the acceptance of these technologies. Data and arguments on the expected risks and benefits of heavy-duty electric trucks, with a special focus on the battery swapping solution, were collected through a survey and expert interviews in the German commercial transport sector. The authors collected a sample of 146 qualitative responses and 61 individual statements on the expected risks and benefits of electric trucks and battery swapping. While the responses to the classified questions are overwhelmingly positive, the individual statements show that there are still many open questions. Full article

Maintaining the accuracy of a sensor system across various operating conditions has always been a challenge, especially for those operating in harsh surroundings such as a radiation environment. Concerning frequency-based sensor interfaces, supply voltage drifts and gain shift of the voltage-to-frequency converter (VFC) are critical design issues. These manifest as gain, offset, and linearity errors at the system level and therefore require continuous correction mechanisms. In this paper, dynamic gain and offset error-compensated open-loop frequency-based sensor interface architectures with adaptive clock frequency are proposed, which result in a ratiometric digital output. To address the mismatch issue, two architectures, one with periodic swapping of the VFCs’ inputs and outputs, and the other with the use of a single analog-to-digital converter (ADC) as an analog front end, are developed. The concepts were demonstrated with implementations on a Zynq board (ZYBO). The results of the first architecture showed that for a 25% gain mismatch between the VFCs, the output gain error was reduced from around 7.4% to 0.79% and the offset error was reduced from around 11.8% to 0.01%. Additionally, for the second architecture, a maximum of 0.11% gain error and 0.1% offset error were recorded for an emulated $\pm$25% supply drift. Full article