Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

We show that every $(n,d,\lambda )$-graph contains a Hamilton cycle for sufficiently large $n$, assuming that $d\geq \log ^{6}n$ and $\lambda \leq cd$, where $c=\frac {1}{70000}$. This significantly improves a recent result of Glock, Correia, and Sudakov, who obtained a similar result for $d$ that grows polynomially with $n$. The proof is based on a new result regarding the second largest eigenvalue of the adjacency matrix of a subgraph induced by a random subset of vertices, combined with a recent result on connecting designated pairs of vertices by vertex-disjoint paths in $(n,d,\lambda )$-graphs. We believe that the former result is of independent interest and will have further applications.

By the reason that mathematical analysis is not feasible for practical control of buildings, decentralized control (DC) and fuzzy control (FC) technologies were introduced to optimize the control problem of high-rise building (HRB) structures. For the control problem of HRB structures, magnetorheological fluid dampers (MRFDs) were introduced to optimize the lateral stress problem of each floor, and the influence of different output variables on FC was compared. In the analysis of fuzzy DC experiments, there were significant differences in the impact of different structural controls (SCs) on building acceleration. In the comparison of the interstory displacement (ISD) time history of the lower concrete structure, the maximum ISD value without control was -12 cm in the nineth second, −7 cm in the nineth second of LQR (linear quadratic regularization) control, and -6 cm in the FC. The proposed biomedical evolutionary technology had better SC effects in practical scenarios, with better safety and stability. The research was mainly based on FC controller technology, and in the future, updated IT2FL (interval type2 fuzzy logic) control technology can be adopted. At the same time, machine learning models are used to optimize parameter problems and improve the control effect of concrete structures. Therefore, fluid dampers help reduce vibrations caused by external earthquakes and other dynamic loads. By dampening devices, fluid dampers enhance the overall stability of the building by improving comfort levels. By allowing for lighter structural designs, fluid dampers can reduce the amount of material needed for construction, leading to cost savings. With reduced vibrations and stresses, there may be fewer maintenance issues over time. Fluid dampers can be designed for various types of structures and can be used in conjunction with other damping systems, making them flexible solutions for different engineering challenges. The future study can be effectively combined with base isolation systems to further improve a building’s resilience against seismic forces.

A tantalizing open problem, posed independently by Stiebitz in 1995 and by Alon in 1996 and again in 2006, asks whether for every pair of integers $s,t \ge 1$ there exists a finite number $F(s,t)$ such that the vertex set of every digraph of minimum out-degree at least $F(s,t)$ can be partitioned into non-empty parts $A$ and $B$ such that the subdigraphs induced on $A$ and $B$ have minimum out-degree at least $s$ and $t$, respectively.

In this short note, we prove that if $F(2,2)$ exists, then all the numbers $F(s,t)$ with $s,t\ge 1$ exist and satisfy $F(s,t)=\Theta (s+t)$. In consequence, the problem of Alon and Stiebitz reduces to the case $s=t=2$. Moreover, the numbers $F(s,t)$ with $s,t \ge 2$ either all exist and grow linearly, or all of them do not exist.

Decentralized consensus protocols have a variety of parameters to be set during their deployment for practical applications in blockchains. The analysis given in most research papers proves the security state of the blockchain, at the same time usually providing a range of acceptable values, thus allowing further tuning of the protocol parameters. In this paper, we investigate Ouroboros Praos, the proof-of-stake consensus protocol deployed in Cardano and other blockchains. In contrast to its predecessor, Praos allows multiple honest slot leaders that lead to fork creation and resolution, consequently decreasing the block rate per time unit. In our analysis of dependence on protocol parameters such as active slot coefficient and p2p network block propagation time, we obtain new theoretical results and explicit formulas for the expectation of the length of the longest chain created during the Praos epoch, the length of the longest unintentional fork created by honest slot leaders, the efficiency of block generation procedure (the ratio of blocks included in the final longest chain vs the total number of created blocks), and other characteristics of the blockchain throughput.

We study these parameters as stochastic characteristics of the block generation process. The model is described in terms of the two-parametric family ξij of independent Bernoulli random variables which generate deformation of the binomial distribution by a positive integer parameter—the delay (deterministic or random). An essential part of our paper is a study of this deformation in terms of denumerable Markov chains and generating functions.

The ongoing Russia-Ukraine war is primarily examined through official narratives, propaganda, and victims’ testimonies. However, the deeper motivations driving Russian men to enlist and fight often remain underexplored. While Western and Ukrainian media frequently attribute this to Russian propaganda, animosity towards Ukrainians, naivety, or financial incentives, these factors only partially capture the issue’s complexity. An additional motive rooted in an enduring ‘behavioral schema’ also plays a significant role. This schema is based on traditional gender roles influencing men’s decisions to engage in combat and women’s decisions to support them. By analyzing Russian social media and combatants’ writings, this research reveals how war discussions are framed by entrenched ‘traditionalist’ behavioral patterns. Utilizing Astrid Erll’s concept of ‘implicit memory’ and James V. Wertsch’s concept of narrative templates, this study elucidates not only the official narratives of the war but also the ‘hidden’ narratives that shape collective feelings and memories.

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

Achieving net-zero carbon emissions by 2050 necessitates the integration of substantial wind power capacity into national power grids. However, the inherent variability and uncertainty of wind energy present significant challenges for grid operators, particularly in maintaining system stability and balance. Accurate short-term forecasting of wind power is therefore essential. This article introduces an innovative framework for regional wind power forecasting over short-term horizons (1–6 h), employing a novel Automated Deep Learning regression framework called WindDragon. Specifically designed to process wind speed maps, WindDragon automatically creates Deep Learning models leveraging Numerical Weather Prediction (NWP) data to deliver state-of-the-art wind power forecasts. We conduct extensive evaluations on data from France for the year 2020, benchmarking WindDragon against a diverse set of baselines, including both deep learning and traditional methods. The results demonstrate that WindDragon achieves substantial improvements in forecast accuracy over the considered baselines, highlighting its potential for enhancing grid reliability in the face of increased wind power integration.

English as a foreign language (EFL) students are increasingly learning English in extramural digital settings (informal digital learning of English; IDLE). Previous research has investigated the antecedents of IDLE engagement, focusing on basic psychological needs (BPNs) in classroom settings. However, little attention has been given to the role of BPNs in digital settings, where digital-native EFL students often fulfil their psychological needs. This study explores the relationship between two core BPNs – competence and relatedness – in both classroom and digital settings and IDLE engagement among 226 Kazakhstani university EFL students. Hierarchical multiple regression analyses indicate that, in the classroom, students who perceive themselves as more competent are more likely to engage in receptive and productive IDLE. Also, a higher sense of in-class relatedness strengthens the positive relationship between in-class competence and productive IDLE. In the digital settings, students who perceive themselves as more competent are more likely to engage in receptive IDLE, while competence alone does not directly lead to productive IDLE. A higher sense of relatedness positively moderates the links, amplifying the connection between competence and engagement in both receptive and productive IDLE. These findings suggest that educators can enhance EFL students’ IDLE engagement by designing and recommending activities that foster competence and a sense of community in both classroom and digital settings.

Robotic lower limb exoskeletons are wearable devices designed to augment human motor functions and enhance physical capabilities mostly adopted in healthcare and rehabilitation. The field is strongly dominated by rigid exoskeletons driven by electromagnetic actuators constituted by electrical motors, gearboxes, and cylinders. This review focuses on the design and specifications of the actuation systems of lower limb exoskeletons, with the ultimate goal of providing reporting guidelines to allow for full reproducibility. For each paper, we assessed the quality and completeness of technical characteristics with two ad hoc rating scales for motors and reducers; we extracted the main parameters of the actuation unit and a quantitative analysis of the mechanical characteristics of the individual components was carried out considering the exoskeleton application. Overall, we observed a lack of details in reporting on actuation systems equipped on exoskeletons. To overcome this limitation, herein we conclude by proposing a data form and a checklist to provide researchers with a common approach in reporting the mechanical characteristics of the actuation unit of their lower limb exoskeletons. We believe that the convergence of exoskeletons’ literature toward a clearer standardization of design and reporting will boost the development of this technology and its diffusion outside the laboratory.