This article explores the intricate dynamics of complexity and emergence through the lens of a graphical model representing hippocampal functions. Utilizing a series of overlapping curves that intersect at varying degrees and frequencies, we create a visual metaphor for the sophisticated processes to store information observed in the hippocampus. This model not only depicts the spatial and frequency-related interactions of these curves but also illustrates the emergent patterns that arise from these interactions. These patterns exemplify the principles of complexity and emergence, where the collective behavior of simple elements results in intricate and unexpected outcomes. This graphical representation serves as a bridge between the abstract mathematical concepts of complexity theory and the tangible biological processes observed in neuroscience. By drawing parallels to the hippocampus, known for its role in memory formation and spatial navigation, the article sheds light on how complex systems, such as neural networks, exhibit properties that are more than just the sum of their parts. The model thus offers a unique perspective on understanding complex systems, not only in neuroscience but in broader scientific inquiries where complexity and emergent phenomena are pivotal.

The Internet of Things (IoT) is a grand vision of connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. The promise is great but the security challenges are also daunting indeed. The IoT security can be addressed from different angles but one of the key issues is the access control model. This would eventually determine which device would be given access to the IoT systems and which would be denied access. In this work, we have done a systematic and thorough survey on the state-of-the-art access control models in IoT. We have covered conventional as well as the advanced access control models taking the crucial period of various studies in this particular area. A number of critical questions have been answered and key works have been summarized. Based on our investigation, some insights into the area, its requirements, scale, and future challenges and prospects have been discussed. We have also provided our rationale and motivation for this work and compared it with previous surveys, none of which has captured this security aspect of IoT with such a depth and detail.

Network Function Virtualization contributes a lot to the networking environments. Many service providers gain benefits from the usage of NFV architecture. NFV works by virtualizing the works and functionality offered by individual hardware networking components, thus making it easily accessible via virtual form. Organizations can reduce the usage of hardware appliances because of NFV’s approach. There are a good number of related studies related to NFV which show people’s interest in this topic. Various research methodologies are used for this study such as researching, data collection, and summarizing. Next, the outstanding performance of NFV leads to providing many benefits such as cost efficiency, flexibility, and reduced energy consumption. However, despite the advantages, NFV also encounters several security challenges. This study will cover the issues of topology risks, complex layers, and malicious insiders. To overcome these challenges, this study suggests strong encryption and authentication, standardized interfaces and APIs, and Zero Trust security architecture.

The intrinsic nature of tic disorders, characterized by symptom variability and fluctuation, poses challenges in clinical evaluations. Currently, tic assessments predominantly rely on subjective questionnaires administered periodically during clinical visits, thus lacking continuous quantitative evaluation. This study aims to establish an automatic objective measure of tic expression in natural behavioral settings. A custom-developed smartphone application was used to record selfie-videos of children and adolescents with tic disorders exhibiting facial motor tics. Facial landmarks were utilized to extract tic-related features from video segments. These features were then passed through a tandem of custom deep neural networks to learn spatial and temporal properties for tic classification. The model achieved a mean accuracy of 95% when trained on data across all subjects, and consistently exceeded 90% accuracy in leave-one-session-out and leave-one-subject-out cross validation training schemes. This automatic tic identification measure may provide a valuable tool for clinicians in facilitating diagnosis, patient follow-up, and treatment efficacy evaluation. Combining this measure with standard smartphone technology has the potential to revolutionize large-scale clinical studies, thereby expediting the development and testing of novel interventions.

It has been conjectured that sounds recorded at the ear, using microphones, originate from either heart sounds propagating from the heart to the recording site, through tissue and bone, or vascular activity at the recording site, such as vasodilation. However, prior studies have not been able to verify these conjectures. The aim of this study is to gain a deeper understanding of the signals measured in the ear using so called body-coupled microphones. Method: A study was conducted on 10 subjects, who were instructed to stand up, lie down and exercise with a body-coupled microphone mounted in each ear, using personalised ear-pieces. The subjects were additionally instructed to follow a guided breathing session. Recorded body-coupled microphone signals were evaluated against standard measures, such as electrocardiography, photoplethysmography, and respiration flow and effort. All signal modalities were synchronised to a common trigger signal. For the analysis, the body-coupled microphone and photoplethysmography signals were epoched in accordance with the electrocardiography R-peak. Results: Cluster-based permutation test showed that the body-coupled microphone signals were time-locked to the electrocardiography R-peak. No difference was found between ears. Discussion: Comparing the timing of the electrocardiography and photoplethysmography events suggests that signals recorded using body-coupled microphones in the ears reflect blood pressure waves in the arteries in the proximity of the body-coupled microphones.

The current large language models (LLMs) mainly lacks this capability: answering  high-precision questions/prompts. LLMs is actually a powerful fuzzy memory system that makes it difficult to answer high-precision questions. The results of code execution is a kind of high-precision answer. And expert system applications need to answer these kinds of questions. In this paper, to solve the above problem, We propose a design of LLMs combined with the text2code approach.

This work presents a robust approach to derive a quasi-optimal ground segment design in Q/V band for providing connectivity to non-geostationary satellites, considering a "N+P" diversity scheme to cope with rain fading. An optimization problem is formulated with the objective of guaranteeing to each satellite a minimum uplink sum-rate while minimizing the total number of GWs via a ℓ0-norm representation. A two-stage algorithm is proposed to tackle the non-convexity of the above and achieve GW diversity. Numerical results demonstrated that the proposal outperforms algorithms in the literature by reducing the required total number of GWs by 47%-67%.

Microscale robotics represents a promising future for minimally invasive medicine. However, one of the biggest challenges of microrobots moving through the human body is represented by the complex 3D structure of biological lumina and tissues, which obstructs the navigation of micron-sized devices. Here, we fabricate ultra-deformable magnetic microrobots, consisting of ferrofluid-loaded lipid vesicles, and we magnetically pull them through chambers that exert upon them a gradually more forceful confinement. We thus analyze their capability to face interstices comparable to or smaller than their characteristic size and their consequent behavior in terms of stability, velocity, and deformation. The results show that the inherent compliance of these vesicle-based magnetic microrobots allows them to infiltrate successfully in interstices slightly smaller than their size. Further enhancement of their compliance and the development of specific control strategies may lead to microrobots able to move through interstices and traverse complex biological environments.This article has been submitted to IEEE Transactions on Medical Robotics and Bionics (manuscript TMRB-07-24-BR-0017) and presented at the IEEE RAS EMBS 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob 2024), 1-4 September 2024 Heidelberg, Germany.

This letter, based on measuring the admittance of a black-boxed grid-following inverter (GFLI) at seven distinct operating points to synthesize an operating-point-parameterized state-space model (OPP SSM), presents an efficient method for analytical prediction of the maximum transferable active power (MTAP) injected by the black-boxed GFLI into the grid under any arbitrary grid condition and reactive power injection level. The proposed method obviates the need for repetitive electromagnetic transient time-domain simulations or frequency-domain impedance criteria by gradually increasing active power output until the MTAP is reached. The feasibility of the synthesized OPP SSM for MTAP prediction has been experimentally validated on a power-controlled GFLI, demonstrating its potential for aiding the integration of inverter-based resources in modern power systems.

The main result of Ref. [1] is put into context of previously published papers on the subject. Its correction is described.

Recently, there has been a growing interest in dielectric antennas and electromagnetic components based on periodic structures, since such components can be realized using new additive manufacturing technologies. The design of these components starts from the geometry of the unit cell, which determines not only the value of the effective refractive index but also the parameters of the entire considered component, like for example, the bandwidth of operation. For this reason, periodic structures with higher symmetry are of particular interest, since they can exhibit a wider bandwidth and other improved parameters. To analyze and design such components, we present a semi-analytical method for calculating the guiding properties of parallel-plate waveguides loaded with periodic glide-symmetric dielectric structures. It is shown that the presence of glide-symmetry can be simply included in the dispersion characteristic equation based on the transverse resonance condition.  The derived expressions reveal that due to glide-symmetry only the Bloch-Floquet modes of the same parity interact and contribute to the dispersion properties. The accuracy of the discussed method is verified through the analysis of several one-dimensional (1-D) and two-dimensional (2-D) structures suitable for the design of planar lens antennas with a large frequency band of operation.

The k-anonymity problem introduced by Samarati and Sweeney in 1998, guarantees that it is impossible to distinguish user data from at least (k − 1) others in the same database. The methods used to achieve k-anonymity result in an information loss as the data in the database is modified, making it less accurate through a process of generalization or micro-aggregation of the stored data. Mauger et al. proposed a O(n²)-time sequential algorithm that gives good results while minimizing the information loss using their designed metrics. However, their solution is very time-consuming and therefore not suitable for large-scale databases. In this paper, we tackle this problem using parallelism. We propose three coarse-grained parallel algorithms to solve the k-anonymity problem. The first is the straightforward algorithm that runs in O(n ²/p) execution time with O(n) communication rounds, where n is the number of lines in the database and p is the number of processors. The second runs in O(n² /p²) execution time with O²(p) communication rounds. The third runs in O(n² /plog2(p)) execution time with O(np/ (log2(p))²) communications rounds. For the latter two algorithms, we introduce the concept of data reorganization to minimize the information loss when data are partitioned. Experimental results show that for a database of size n = 10^6 , p = 2^7 , and k = 10^2 , second, and third parallel algorithms are respectively 1127.59× and 41.13× faster than the sequential algorithm while achieving anonymity with 4.03% and 2.62% information loss.

This research presents a network-based simulation approach to model the impacts of species extinction and habitat loss on ecological networks, with a particular focus on the Amazon ecosystem. By constructing weighted directed networks that represent species interactions, we identify keystone species through centrality measures and assess their critical roles in maintaining ecosystem stability. Using community detection algorithms, we uncover the modular structure of these interactions, revealing key patterns of interdependence within the network. Novel simulations of environmental disturbances—such as habitat fragmentation, patch loss, and species migration—further explore how these disruptions affect network structure, biodiversity, and overall ecosystem resilience. The findings offer valuable insights into ecosystem vulnerability and provide actionable guidance for conservation strategies aimed at mitigating biodiversity loss in fragile ecosystems.

The escalating threat of climate change necessitates urgent and comprehensive action to mitigate its impacts. Renewable energy technologies, such as solar, wind, and hydroelectric power, offer sustainable alternatives to fossil fuels, significantly reducing greenhouse gas emissions. Concurrently, the adoption of EVs presents a viable solution to curbing emissions from the transportation sector, one of the largest contributors to global warming. However, the deployment of renewables and EVs, especially in electrical distribution systems, poses significant challenges - particularly in terms of loading and voltage violations, as well as of power quality deterioration - representing one of the major limitations to their large-scale deployment. Without careful planning and control, such deployment may be technically unfeasible or necessitate extensive and costly upgrades to the distribution infrastructure, potentially affecting up to 95% of existing assets. By analyzing a case study in Seychelles the paper provide a framework for sizing EV, battery storage systems (BESS) and photovoltaic (PV) with a clear focus in eliminating the needs of costly grid reinforcements accelerating a substantial reduction in carbon footprint. Specifically, our approach strikes a balance between grid impact and performance, resulting in a sustainable solution for both customers and system operators. The findings also underscore the necessity for policies to support the proposed solutions.

The flashover in vacuum is a rapid interfacial discharge across the insulator surface when subjected to high applied voltage. Here a theoretical model covering above-surface processes is introduced. The model calculates the flashover threshold in vacuum, with revised secondary electron emission avalanche theory and improved desorbed neutral transport model. The model serves as the first part of an integrated flashover model, aiming for consistent treatment of both above-surface and subsurface processes during the entire flashover development. The flashover threshold is obtained by combining the desorbed neutral pressure at given applied voltage and the gas breakdown criterion dictated by the Paschenâ\euro™s law. An analytical formula for threshold estimation containing physical parameters, and a simplified formula consisting of empirical coefficients are introduced, catering for both conceptual understanding and practical application. The derived formulae are validated by experimental data for a variety of insulator materials. The theory generalization for nonuniform electric field distribution is further discussed.

In industry deep learning application, our manually labeled dataset has a certain number of noisy data. In the previous works, we verify that manual re-labeling the training data that model prediction do not equal to the manual label, improve the accuracy at dev dataset and human evaluation. But manual re-labeling requires a lot of human labor to correct the noisy data in the training dataset. We think that the noisy data is the training data that model prediction and manual label is not equal. To solve this problem, we propose the randomly re-setting method that automaticly improve the accuracy at dev dataset and human evaluation. The core idea is that, we randomly re-set all the noisy data to model prediction or the manual label. All the noisy data means the noisy data in training dataset and dev dataset. We use the highest accuracy of dev dataset in the all possible randomly re-setted dev datasets, to judge the quality of randomly resetted training datasets. The motivation of randomly re-setting is that the best dataset must be in all randomly possible datasets.

Mixed Reality (MR) and Artificial Intelligence (AI) are increasingly becoming integral parts of our daily lives. Their applications range in fields from healthcare to education to entertainment. MR has opened a new frontier for such fields as well as new methods of enhancing user engagement. In this paper, We propose a new system one that combines the power of Large Language Models (LLMs) and mixed reality (MR) to provide a personalized companion for educational purposes. We present an overview of its structure and components as well tests to measure its performance. We found that our system is better in generating coherent information, however it's rather limited by the documents provided to it. This interdisciplinary approach aims to provide a better user experience and enhance user engagement. The user can interact with the system through a custom-design smart watch, smart glasses and a mobile app.

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