Search Results (15,972)

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

This study aims to evaluate the level of awareness and practical adoption of green wall systems in the Nigerian construction industry. It seeks to examine the current state of green wall implementation and recommend strategies to enhance their integration into construction practices among Nigerian construction professionals. A thorough review of the existing literature was conducted to identify different types of green wall systems. Insights from this review informed the design of a structured questionnaire, which was distributed to construction professionals based in Lagos State. The data collected were analyzed using statistical tests. The study reveals that while there is generally high awareness of green wall systems among Nigerian construction professionals, the practical use remains low, with just 8 out of the 18 systems being actively implemented, eclipsing the mean value of 3.0. The findings underscore the need for targeted education, industry incentives, and increased advocacy to encourage the use of green wall systems in the Nigerian construction sector. The results have significant implications for the Nigerian construction industry. The limited awareness and adoption of green wall systems highlight the need for strategic actions from policymakers, industry leaders and educational institutions. Promoting the use of green walls could drive more sustainable building practices, improve environmental outcomes and support the broader goals of decarbonization and circularity in construction. This research adds to the body of knowledge on sustainable construction by offering a detailed evaluation of green wall awareness and adoption within the Nigerian context. While green wall systems have been studied globally, this research provides a regional perspective, which in this case focuses on Lagos State. The study’s recognition of the gap between awareness and implementation highlights an important area for future research and industry development. Full article

Approximately 185,000 people in the United states experience limb loss each year. There is a need for an intuitive neural interface that can offer high-fidelity control signals to optimize the advanced functionality of prosthetic devices. Regenerative peripheral nerve interface (RPNI) is a pioneering advancement in neuroengineering that combines surgical techniques with biocompatible materials to create an interface for individuals with limb loss. RPNIs are surgically constructed from autologous muscle grafts that are neurotized by the residual peripheral nerves of an individual with limb loss. RPNIs amplify neural signals and demonstrate long term stability. In this narrative review, the terms “Regenerative Peripheral Nerve Interface (RPNI)” and “RPNI surgery” are used interchangeably to refer to the same surgical and biological construct. This narrative review specifically focuses on RPNIs as a targeted approach to enhance prosthetic control through surgically created nerve–muscle interfaces. This area of research offers a promising solution to overcome the limitations of existing prosthetic control systems and could help improve the quality of life for people suffering from limb loss. It allows for multi-channel control and bidirectional communication, while enhancing the functionality of prosthetics through improved sensory feedback. RPNI surgery holds significant promise for improving the quality of life for individuals with limb loss by providing a more intuitive and responsive prosthetic experience. Full article

This contribution aims to honour Guido Barbiellini’s profound interest in the interpretation and impact of quantum mechanics by examining the implications of the so-called before–before Experiment on quantum entanglement. This experiment was inspired by talks and discussions with John Bell at CERN. This was during the years when John and Guido co-worked, promoting the mission of the laboratory: “to advance the boundaries of human knowledge”. As the experiment uses measuring devices in motion, it can be considered a complement to entanglement experiments using stationary measuring devices, which have meanwhile been awarded the 2022 Nobel Prize in Physics. The before–before Experiment supports the idea that the quantum realm exists beyond space and time and that the quantum state is a real mental entity concerning choices. As it also leads us to a better understanding of the ‘quantum collapse’ and the measurement process, we pay homage to Guido’s work on detectors, such as his collaborations on the DELPHI experiment at CERN, on cosmic ray detection at the International Space Station, and gamma-ray astrophysics during a large NASA space mission. Full article

Pimelea is one of the highly toxic plants in Australia, particularly affecting cattle. It contains simplexin, a potent toxin that can cause Pimelea poisoning (St. George Disease) in livestock. A survey was conducted to assess the current impact of Pimelea on livestock production, pasture systems, and financial losses among agricultural producers. In addition, information was also sought about the environmental conditions that facilitate its growth and the effectiveness of existing management strategies. The survey responses were obtained from producers affected by Pimelea across nine different Local Government Areas, through three States, viz., Queensland, New South Wales, and South Australia. Pimelea was reported to significantly affect animal production, with 97% of producers surveyed acknowledging its detrimental effects. Among livestock, cattle were the most severely affected (94%), when compared to sheep (13%), goats (3%), and horses (3%). The presence of Pimelea was mostly observed in spring (65%) and winter (48%), although 29% of respondents indicated that it could be present all year-round under favorable rainfall conditions. Germination was associated with light to moderate rainfall (52%), while only 24% linked it to heavy rainfall. Pimelea simplex F. Muell. was the most frequently encountered species (71%), followed by Pimelea trichostachya Lindl. (26%). Infestations were reported to occur annually by 47% of producers, with 41% noting occurrences every 2 to 5 years. Financially, producers estimated average annual losses of AUD 67,000, with 50% reporting an average of 26 cattle deaths per year, reaching up to 105 deaths in severe years. Some producers were spending up to AUD 2100 per annum to manage Pimelea. While chemical and physical controls were commonly employed, integrating competitive pastures and alternative livestock, such as sheep and goats, was considered as a potential management strategy. This study reiterates the need for further research on sustainable pasture management practices to reduce Pimelea-related risks to livestock and agricultural production systems. Full article

Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods. Full article

Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

Multimodal sentiment analysis (MSA) benefits from integrating diverse modalities (e.g., text, video, and audio). However, challenges remain in effectively aligning non-text features and mitigating redundant information, which may limit potential performance improvements. To address these challenges, we propose a Hierarchical Text-Guided Refinement Network (HTRN), a novel framework that refines and aligns non-text modalities using hierarchical textual representations. We introduce Shuffle-Insert Fusion (SIF) and the Text-Guided Alignment Layer (TAL) to enhance crossmodal interactions and suppress irrelevant signals. In SIF, empty tokens are inserted at fixed intervals in unimodal feature sequences, disrupting local correlations and promoting more generalized representations with improved feature diversity. The TAL guides the refinement of audio and visual representations by leveraging textual semantics and dynamically adjusting their contributions through learnable gating factors, ensuring that non-text modalities remain semantically coherent while retaining essential crossmodal interactions. Experiments demonstrate that the HTRN achieves state-of-the-art performance with accuracies of 86.3% (Acc-2) on CMU-MOSI, 86.7% (Acc-2) on CMU-MOSEI, and 80.3% (Acc-2) on CH-SIMS, outperforming existing methods by 0.8–3.45%. Ablation studies validate the contributions of SIF and the TAL, showing 1.9–2.1% performance gains over baselines. By integrating these components, the HTRN establishes a robust multimodal representation learning framework. Full article

While existing studies document physical and economic impacts of new-build gentrification, the temporally protracted trauma of indirect displacement in communities adjacent to redeveloped areas remains understudied. Employing constructivist grounded theory, this study asks the following question: how do residents experience place attachment erosion during prolonged policy suspension in Shanghai’s new-build gentrification? Through iterative analysis of 25 interviews, we reveal a temporal vicious cycle of waiting triggered by uneven redevelopment and policy inertia. This cycle systematically dismantles belonging through several mechanisms: (1) chronic place-identity deterioration; (2) progressive social network fragmentation; (3) the collapse of imagined futures; and (4) the ambiguous loss of place attachment—where physical presence coexists with psychological disengagement. Crucially, we redefine indirect displacement as a temporal erosion of place identity and attachment, revealing a paradoxical state of physical presence coexisting with psychological disengagement. This paper provides a new perspective for better understanding the different dimensions of indirect displacement in new-build gentrification, which will help inform equitable development efforts that are more inclusive and just. Full article

Real-time traffic light detection is essential for the safe navigation of autonomous vehicles, where timely and accurate recognition of signal states is critical. YOLOv8, a state-of-the-art object detection model, offers enhanced speed and precision, making it well-suited for real-time applications in complex driving environments. This study presents a modified YOLOv8 architecture optimized for traffic light detection by integrating Depth-Wise Separable Convolutions (DWSCs) throughout the backbone and head. The model was first pretrained on a public traffic light dataset to establish a strong baseline and then fine-tuned on a custom real-time dataset consisting of 480 images collected from video recordings under diverse road conditions. Experimental results demonstrate high detection performance, with precision scores of 0.992 for red, 0.995 for yellow, and 0.853 for green lights. The model achieved an average mAP@0.5 of 0.947, with stable F1 scores and low validation losses over 80 epochs, confirming effective learning and generalization. Compared to existing YOLO variants, the modified architecture showed superior performance, especially for red and yellow lights. Full article

In this study, we investigate the nonlinear dynamical behavior of a one-dimensional linear piecewise-smooth discontinuous (LPSD) map with a negative slope, motivated by its occurrence in systems exhibiting discontinuities, such as power electronic converters. The objective of the proposed research is to develop an analytical approach. Analytical conditions are derived for the existence of stable period-1 and period-2 orbits within the third quadrant of the parameter space defined by slope coefficients $a<0$ and $b<0$. The coexistence of multiple attractors is demonstrated. We also show that a novel class of orbits exists in which both points lie entirely in either the left or right domain. These orbits are shown to eventually exhibit periodic behavior, and a closed-form expression is derived to compute the number of iterations required for a trajectory to converge to such orbits. This method also enhances the ease of analyzing system stability by mapping the state–variable dynamics using a non-smooth discontinuous map. The analytical findings are validated using bifurcation diagrams, cobweb plots, and basin of attraction visualizations. Full article

A brief review of various existing mathematical formulations of the uncertainty relations in quantum mechanics, containing variances of two or more non-commuting operators, is given. In particular, inequalities for the products of higher-order moments of a coordinate and a momentum are considered, as well as inequalities making the uncertainty relations more accurate when additional information about a quantum system is available (for example, the correlation coefficient or the degree of mixing of a quantum state characterized by the trace of the squared statistical operator). The special cases of two, three, and four operators are discussed in detail. Full article

Nuclear power is a low-emission and economically competitive energy source, yet the effective disposal and management of its associated radioactive waste can be challenging. Radioactive waste can be categorised as high-level waste (HLW), intermediate-level waste (ILW), and low-level waste (LLW). LLW primarily comprises materials contaminated during routine clean-up, such as mop heads, paper towels, and floor sweepings. While LLW is less radioactive compared to HLW and ILW, the management of LLW poses significant challenges due to the large volume that requires processing and disposal. The volume of LLW can be significantly reduced through sorting, which is typically performed manually in a labour-intensive way. Smart management techniques, such as computer vision (CV) and machine learning (ML), have great potential to help reduce the workload and human errors during LLW sorting. This paper provides a comprehensive review of previous research related to LLW sorting and a summative review of existing applications of CV in solid waste management. It also discusses state-of-the-art CV and ML algorithms and their potential for automating LLW sorting. This review lays a foundation for and helps facilitate the applications of CV and ML techniques in LLW sorting, paving the way for automated LLW sorting and sustainable LLW management. Full article

Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning models have been developed, they demonstrate significant limitations. An interpretable and improved transfer learning model for binary skin cancer classification is proposed in this research, which uses the last convolutional block of VGG16 as the feature extractor. The methodology focuses on addressing the existing limitations in skin cancer classification, to support dermatologists and potentially saving lives through advanced, reliable, and accessible AI-driven diagnostic tools. Explainable AI is incorporated for the visualization and explanation of classifications. Multiple optimization techniques are applied to avoid overfitting, ensure stable training, and enhance the classification accuracy of dermoscopic images into benign and malignant classes. The proposed model shows 90.91% classification accuracy, which is better than state-of-the-art models and established approaches in skin cancer classification. An interactive desktop application integrating the model is developed, enabling real-time preliminary screening with offline access. Full article