Search Results (4,052)

Background/Objectives: Previous research has suggested that Western musicians, who generally demonstrate proficiency in reading musical scores, exhibit superior performance in visuospatial working memory tasks compared to non-musicians. Evidence indicates brain activation in regions such as the left inferior parietal lobe and the right posterior fusiform gyrus during music reading, which are associated with visuospatial processing. This study aimed to explore how musical training influences spatial working memory and to examine the relationship between self-assessment accuracy and cognitive performance. Methods: A visuospatial working memory test, the Corsi block-tapping test (CBT), was administered to 70 participants, including 35 musicians with experience in music reading and 35 non-musicians. CBT performances were compared between groups, controlling for sex and age differences using analysis of covariance. Participants were also asked to self-assess their visuospatial capabilities. Results: Musicians performed significantly better than non-musicians in the CBT and demonstrated greater metacognitive accuracy in evaluating their visuospatial memory capacities. A total of 46.34% of musicians who claimed good performance on the CBT did in fact perform well, in comparison with 14.63% of non-musicians. Sex influenced the outcomes of spatial working memory, while age did not significantly affect performance. Conclusions: This self-awareness of visuospatial capabilities reflects a form of metacompetence, encompassing reflective thinking and the ability to assess one’s cognitive skills. Furthermore, while differences in spatial working memory between musicians and non-musicians appear to be related to executive functions associated with general music practice, further investigation is needed to explore other potential influences beyond musical experience. Full article

Coverage Path Planning (CPP) in unknown environments presents unique challenges that often require the system to maintain a symmetry between exploration and exploitation in order to efficiently cover unknown areas. This paper introduces latent imagination-based reinforcement learning (LIRL), a novel framework that addresses these challenges by integrating three key components: memory-augmented experience replay (MAER), a latent imagination module (LIM), and multi-step prediction learning (MSPL) within a soft actor–critic architecture. MAER enhances sample efficiency by prioritizing experience retrieval, LIM facilitates long-term planning via simulated trajectories, and MSPL optimizes the trade-off between immediate rewards and future outcomes through adaptive n-step learning. MAER, LIM, and MSPL work within a soft actor–critic architecture, and LIRL creates a dynamic equilibrium that enables efficient, adaptive decision-making. We evaluate LIRL across diverse simulated environments, demonstrating substantial improvements over state-of-the-art methods. Through this method, the agent optimally balances short-term actions with long-term planning, maintaining symmetrical responses to varying environmental changes. The results highlight LIRL’s potential for advancing autonomous CPP in real-world applications such as search and rescue, agricultural robotics, and warehouse automation. Our work contributes to the broader fields of robotics and reinforcement learning, offering insights into integrating memory, imagination, and adaptive learning for complex sequential decision-making tasks. Full article

In contemporary vehicle applications, lithium-ion batteries have become a leading option among the diverse array of battery technologies available. This preference is attributed to their advantageous properties, which include low self-discharge rates and no memory effect. Despite these benefits, lithium-ion batteries are not without their challenges. The key issues include a restricted driving range, concerns regarding longevity, safety risks, and prolonged charging durations. Efforts aimed at minimizing the charging duration frequently entail the introduction of elevated currents into the battery, a practice that can significantly elevate its temperature and, in turn, diminish its operational lifespan. Generally, battery packs in electric vehicles are equipped with flat cooling plates located on their side or bottom surfaces, which also serve the dual purpose of providing heating in colder conditions. Nevertheless, this cooling configuration faces difficulties during fast charging and may not efficiently heat or cool the batteries. In this work, a novel thermal management approach is proposed, in which a battery module is cooled not only with a bottom cooling plate but also using another cooling plate in contact with the busbars, located on the top of the battery module. The simulations and experimental tests show that this new configuration demonstrates significant improvements. The thermal time constant is reduced by 47%, enabling faster cooling of the module. Additionally, the maximum temperature reached by the battery during charging with dual cooling is lowered by 6 °C compared to the conventional approach. In this configuration, the top cooling plate acts as a thermal bridge. This is a key advantage that promotes temperature homogenization within the battery module. As a result, it supports an even aging process of batteries, ensuring their longevity and optimal performance. Full article

Conventional energy-absorbing components have limited adjustability under complex working conditions. To overcome this limitation, we designed a bio-inspired sinusoidal metamaterial (BSM) inspired by the efficient energy-absorbing structure of the mantis shrimp jaw foot and 4D printed it based on shape-memory polymer (SMP). The effects of single-cell structural parameters and gradient design on its force–displacement curves and energy-absorbing properties were explored. Based on the shape memory effect of SMP, the BSM can obtain arbitrary temporary shapes under the combined effect of temperature and force, realizing locally controllable compression deformation and programmable mechanical properties of the BSM structure. This research has a broad application prospect in the field of energy absorption and energy management and provides new ideas for the design of smart structural materials. Full article

Memory impairment is caused by the absence of the 4E-BP2 protein in the brain. This protein undergoes deamidation spontaneously in the neurons. 4E-BP2 deamidation significantly alters protein synthesis in the neurons and affects the balance of protein production required for a healthy nervous system. Any imbalance in protein production in the nervous system causes neurodegenerative diseases. Discovering what causes 4E-BP2 deamidation will make it possible to control this balance of protein production and develop effective treatments against neurodegenerative diseases such as Alzheimer’s and Parkinson’s. The purpose of this work is to discover the neurobiological mechanism that causes the deamidation reaction in the 4E-BP2 protein by performing immunoblotting in the retinal ganglia, the optic nerve, the dorsal root ganglia, the sciatic nerve, and the whole brain, extracted via dissection from 2-month-old, Wild-type male mice. The results show that axons and their unique properties cause neuron-specific 4E-BP2 deamidation in the nervous system, confirming conclusively that axons are the critical factors behind the fundamental neurobiological mechanism of 4E-BP2 protein deamidation. Full article

Accurate earthwork quantity estimation is essential for effective project planning and cost management in the Architecture, Engineering, and Construction (AEC) industry. Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. This study introduces the Earthwork Network Architecture (ENA), a novel deep learning framework that incorporates Large Language Models (LLMs), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM) networks, and Transformers to automate and enhance the accuracy of earthwork quantity estimation. We assume that if LLMs can be trained effectively using such unstructured construction dataset, the effects such as improved accuracy and the challenges of LLMs can be clearly examined. Among the architectures tested, the LLM-based ENA demonstrated superior performance, achieving faster convergence, greater loss reduction, and higher classification accuracy, with a Quantity Takeoff Classification accuracy of 97.17%. However, the LLMs required significantly more computational resources compared with other models. These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. This study lays the groundwork for future AI-driven solutions in the AEC industry, underscoring the potential of LLMs and Transformers to automate the quantity takeoff process and manage multimodal data in construction projects. Full article

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications. Full article

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system’s immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system’s immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified. Full article

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models. Full article

It is not unusual for government reports to place spatial inequalities into a league table, highlighting poorly performing jurisdictions in some form of yardstick competition. A case in point is long-term dwelling vacancies. Action on Empty Homes, which works closely with local governments in the UK, provides commentary about poor performance, including recording persistently higher rates in certain regions and mentioning the worst rankings at the local level. How the distribution of vacant dwellings changes is not well explored. How rigid this league is has been a feature of a discussion in the growth literature. A useful dynamic policy measure in this regard is how far the average jurisdiction changes its league position over a given period. A type of convergence proposed here, implied by Sala-i-Martin, features the time for the initial rankings to become discordant with the current order. One could see this sort of measure being used widely, wherever performances are judged relatively as a means of highlighting good practice. Using English data on vacant dwellings, this paper shows that there is a ‘long memory’ in long-term vacancy rates, both at the local and regional levels. The industrial and housing inheritances of districts in the North of England, evident in the league table in 2004, remain influential after 18 years, even with general population growth. Although the use of incentives to bring dwellings back into use is thought to be successful, it is suggested that the role of the buy-to-let investor may have been overlooked. Changes in the treatment of their rewards appear to coincide with a rise in long-term vacancies. Full article

Background: Enhancing working memory performance in cognitively and physically healthy individuals is a popular area of research. The results from a large number of studies have now been summarized in multiple meta-analyses. In these reviews, various training methods have been examined, including mindfulness training, adaptive working memory training, physical activity training, and video game training, to examine whether working memory capacity can be improved. This report aggregated the results of these meta-analyses using second-order meta-analytic approaches to ascertain the extent to which working memory functioning can be enhanced in healthy adults. Methods: A total of six meta-analyses of randomized controlled trials that compared working memory interventions to a control group were included in the analyses. These studies were identified after systematically searching three electronic databases: APA PsycInfo, ERIC and Medline. Collectively, the meta-analyses investigated the effects of cognitive programs, mindfulness, video games and physical activity on working memory. Only meta-analyses undertaken with healthy adults aged between 18 and 55 years were included in the report. Results: The results revealed an average improvement in working memory across the included studies compared to the control groups. The findings indicated a small yet significant enhancement in working memory, with a standardized mean difference of 0.335 (95% CI [0.223; 0.447], p < .001). Further analysis tests for superiority effects between the different working memory training programs revealed no significant differences between intervention effect sizes. Conclusion: Based on the findings, it can be concluded that the working memory capacity of healthy adults can be improved through training. However, the effect size is small, so the utility of this type of training in real-life improvements in cognition may be minimal. The evidence does not indicate that one type of working memory training is superior to another. Full article

Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. Full article

Financial forecasting plays a critical role in decision-making across various economic sectors, aiming to predict market dynamics and economic indicators through the analysis of historical data. This study addresses the challenges posed by traditional forecasting methods, which often struggle to capture the complexities of financial data, leading to suboptimal predictions. To overcome these limitations, this research proposes a hybrid forecasting model that integrates Bayesian optimization with Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the accuracy of market trend and asset price predictions while improving the robustness of forecasts for economic indicators, which are essential for strategic positioning, risk management, and policy formulation. The methodology involves leveraging the strengths of both Bayesian optimization and LSTM networks, allowing for more effective pattern recognition and forecasting in volatile market conditions. Key contributions of this work include the development of a novel hybrid framework that demonstrates superior performance with significantly reduced forecasting errors compared to traditional methods. Experimental results highlight the model’s potential to support informed decision-making amidst market uncertainty, ultimately contributing to improved market efficiency and stability. Full article

In this work, the seismic response of a multi-story, multi-bay special truss moment frame (STMF) with Ni-Ti shape memory alloys (SMAs) incorporated in the form of X-diagonal braces in the special segment is investigated. The diameter of the SMAs per diagonal in each floor was initially determined, considering the expected ultimate strength of the special segment, developed when the frame reaches its target drift and the desirable collapse mechanism, i.e., the formation of plastic hinges, according to the performance-based plastic design procedure. To further investigate the response of the structure with the SMAs incorporated, half the calculated SMA diameters were introduced. Continuing, three more cases were investigated: the mean value of the SMA diameter was introduced at each floor (case DC1), half the SMA diameter of case DC1 (case DC2), and twice the SMA diameter of case DC1 (case CD3). Dynamic time history analyses under seven benchmark earthquakes were conducted using commercial nonlinear Finite Element software (SeismoStruct 2024). Results were presented in the form of top-displacement time histories, the SMAs force–displacement curves, and maximum inter-story drifts, calculating also maximum SMA displacements. The analysis outcomes highlight the potential of the SMAs to be considered as a novel material in the seismic retrofit of steel structures. Both design approaches presented exhibit a certain amount of effectiveness, depending on the distribution, with the placement of the SMA bars and the seismic excitation considered. Further research is suggested to fully understand the capabilities of the use of SMAs as dissipation devices in steel structures. Full article

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount. Full article