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Search Results (1,171)

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19 pages, 4452 KiB  
Article
Artificial Surface Water Construction Aggregated Water Loss Through Evaporation in the North China Plain
by Ziang Wang, Yan Zhou, Wenge Zhang, Shimin Tian, Yaoping Cui, Haifeng Tian, Xiaoyan Liu and Bing Han
Remote Sens. 2025, 17(15), 2698; https://doi.org/10.3390/rs17152698 - 4 Aug 2025
Abstract
As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of [...] Read more.
As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km3/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions. Full article
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32 pages, 1986 KiB  
Article
Machine Learning-Based Blockchain Technology for Secure V2X Communication: Open Challenges and Solutions
by Yonas Teweldemedhin Gebrezgiher, Sekione Reward Jeremiah, Xianjun Deng and Jong Hyuk Park
Sensors 2025, 25(15), 4793; https://doi.org/10.3390/s25154793 - 4 Aug 2025
Abstract
Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and [...] Read more.
Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats. Full article
(This article belongs to the Section Vehicular Sensing)
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24 pages, 650 KiB  
Article
Investigating Users’ Acceptance of Autonomous Buses by Examining Their Willingness to Use and Willingness to Pay: The Case of the City of Trikala, Greece
by Spyros Niavis, Nikolaos Gavanas, Konstantina Anastasiadou and Paschalis Arvanitidis
Urban Sci. 2025, 9(8), 298; https://doi.org/10.3390/urbansci9080298 - 1 Aug 2025
Viewed by 243
Abstract
Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in [...] Read more.
Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article
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30 pages, 9514 KiB  
Article
FPGA Implementation of Secure Image Transmission System Using 4D and 5D Fractional-Order Memristive Chaotic Oscillators
by Jose-Cruz Nuñez-Perez, Opeyemi-Micheal Afolabi, Vincent-Ademola Adeyemi, Yuma Sandoval-Ibarra and Esteban Tlelo-Cuautle
Fractal Fract. 2025, 9(8), 506; https://doi.org/10.3390/fractalfract9080506 - 31 Jul 2025
Viewed by 171
Abstract
With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and [...] Read more.
With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and resistance to attacks. Advances in fractional calculus and memristive technologies offer new avenues for enhancing security through more complex and tunable dynamics. However, the practical deployment of high-dimensional fractional-order memristive chaotic systems in hardware remains underexplored. This study addresses this gap by presenting a secure image transmission system implemented on a field-programmable gate array (FPGA) using a universal high-dimensional memristive chaotic topology with arbitrary-order dynamics. The design leverages four- and five-dimensional hyperchaotic oscillators, analyzed through bifurcation diagrams and Lyapunov exponents. To enable efficient hardware realization, the chaotic dynamics are approximated using the explicit fractional-order Runge–Kutta (EFORK) method with the Caputo fractional derivative, implemented in VHDL. Deployed on the Xilinx Artix-7 AC701 platform, synchronized master–slave chaotic generators drive a multi-stage stream cipher. This encryption process supports both RGB and grayscale images. Evaluation shows strong cryptographic properties: correlation of 6.1081×105, entropy of 7.9991, NPCR of 99.9776%, UACI of 33.4154%, and a key space of 21344, confirming high security and robustness. Full article
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15 pages, 443 KiB  
Article
Prematurity and Low Birth Weight Among Food-Secure and Food-Insecure Households: A Comparative Study in Surabaya, Indonesia
by Arie Dwi Alristina, Nour Mahrouseh, Anggi Septia Irawan, Rizky Dzariyani Laili, Alexandra Vivien Zimonyi-Bakó and Helga Judit Feith
Nutrients 2025, 17(15), 2479; https://doi.org/10.3390/nu17152479 - 29 Jul 2025
Viewed by 186
Abstract
Background: Prematurity and low birth weight (LBW) drive infant morbidity and mortality, requiring nutritional interventions, especially in food-insecure settings. In Indonesia, regional disparities in food security hinder adequate nutrition for premature and LBW infants, exacerbating health challenges. The aim of study is [...] Read more.
Background: Prematurity and low birth weight (LBW) drive infant morbidity and mortality, requiring nutritional interventions, especially in food-insecure settings. In Indonesia, regional disparities in food security hinder adequate nutrition for premature and LBW infants, exacerbating health challenges. The aim of study is to investigate and determine factors associated with prematurity and LBW in children from food-insecure and food-secure households. Methods: This research employed a cross-sectional study with 657 mothers of children aged 36–59 months, conducted using random sampling. Data was collected via standardized questionnaires and analyzed using Chi-square tests and logistic regression. Results: The adjusted model showed that children of food-insecure households had a higher risk of LBW (AOR = 0.54; 95% CI: 0.29–0.99; p < 0.05). LBWs were found to significantly less occur in food-insecure households. Low maternal education was associated with an increased risk of preterm birth (AOR = 3.23; 95% CI:1.78–5.84; p < 0.001). Furthermore, prematurity correlated with house ownership (p < 0.01), indicating the household’s wealth condition. Maternal education and house ownership were linked to prematurity, indicating the risk to child health outcomes. In summary, maternal education, employment status, and household income were linked to food insecurity, indicating the risk to child health outcomes. Conclusion: Strategies to improve child health outcomes are essential, including enhancing maternal nutrition knowledge to improve child feeding practices, promoting gender equality in career development, and reducing food insecurity in households. Full article
(This article belongs to the Section Pediatric Nutrition)
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24 pages, 32703 KiB  
Article
Spatiotemporal Evolution of Carbon Storage and Driving Factors in Major Sugarcane-Producing Regions of Guangxi, China
by Jianing Ma, Jun Wen, Shirui Du, Chuanmin Yan and Chuntian Pan
Agronomy 2025, 15(8), 1817; https://doi.org/10.3390/agronomy15081817 - 27 Jul 2025
Viewed by 224
Abstract
Objectives: The major sugarcane-producing regions of Guangxi represent a critical agricultural zone in China. Investigating the mechanisms of land use change and carbon storage dynamics in this area is essential for optimizing regional ecological security and promoting sustainable development. Methods: Employing the land [...] Read more.
Objectives: The major sugarcane-producing regions of Guangxi represent a critical agricultural zone in China. Investigating the mechanisms of land use change and carbon storage dynamics in this area is essential for optimizing regional ecological security and promoting sustainable development. Methods: Employing the land use transfer matrix, the InVEST model and the Geodetector model to analyze carbon storage changes and identify key driving factors and their interactive effects. Results: (1) From 2011 to 2022, Guangxi’s major sugarcane-producing regions experienced significant land use changes: reductions in cultivated land, grassland and water bodies alongside expansions of forest, bare land and construction land. (2) The total carbon storage in Guangxi’s major sugarcane-producing regions has increased from 2011 to 2018 by 0.99%, representing 1627.03 and 1643.10 million tons, while it has decreased by 0.1% in 2022 (1641.47 million tons) compared to 2018. (3) Cultivated land proportion and forest coverage rate were the primary drivers of spatial heterogeneity, followed by average slope and land urbanization rate. (4) Interaction analysis revealed strong synergistic effects among cultivated land proportion, forest coverage rate, NDVI and average slope, confirming multi-factor control over carbon storage changes. Conclusions: Carbon storage in the Guangxi sugarcane-producing regions is shaped by land use patterns and multi-factor interactions. Future strategies should optimize land use structures and balance urbanization with ecological protection to enhance regional carbon sequestration. Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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17 pages, 274 KiB  
Article
“I Shouldn’t Have to Drive to the Suburbs”: Grocery Store Access, Transportation, and Food Security in Detroit During the COVID-19 Pandemic
by Aeneas O. Koosis, Alex B. Hill, Megan Whaley and Alyssa W. Beavers
Nutrients 2025, 17(15), 2441; https://doi.org/10.3390/nu17152441 - 26 Jul 2025
Viewed by 295
Abstract
Objective: To explore the relationship between type of grocery store used (chain vs. independent), transportation access, food insecurity, and fruit and vegetable intake in Detroit, Michigan, USA, during the COVID-19 pandemic. Design: A cross-sectional online survey was conducted from December 2021 to May [...] Read more.
Objective: To explore the relationship between type of grocery store used (chain vs. independent), transportation access, food insecurity, and fruit and vegetable intake in Detroit, Michigan, USA, during the COVID-19 pandemic. Design: A cross-sectional online survey was conducted from December 2021 to May 2022. Setting: Detroit, Michigan. Participants: 656 Detroit residents aged 18 and older. Results: Bivariate analyses showed that chain grocery store shoppers reported significantly greater fruit and vegetable intake (2.42 vs. 2.14 times/day for independent grocery store shoppers, p < 0.001) and lower rates of food insecurity compared to independent store shoppers (45.9% vs. 65.3% for independent grocery store shoppers, p < 0.001). Fewer independent store shoppers used their own vehicle (52.9% vs. 76.2% for chain store shoppers, p < 0.001). After adjusting for socioeconomic and demographic variables transportation access was strongly associated with increased odds of shopping at chain stores (OR = 1.89, 95% CI [1.21,2.95], p = 0.005) but food insecurity was no longer associated with grocery store type. Shopping at chain grocery stores was associated with higher fruit and vegetable intake after adjusting for covariates (1.18 times more per day, p = 0.042). Qualitative responses highlighted systemic barriers, including poor food quality, high costs, and limited transportation options, exacerbating food access inequities. Conclusions: These disparities underscore the need for targeted interventions to improve transportation options and support food security in vulnerable populations, particularly in urban areas like Detroit. Addressing these structural challenges is essential for reducing food insecurity and promoting equitable access to nutritious foods. Full article
(This article belongs to the Section Nutrition and Public Health)
21 pages, 9651 KiB  
Article
Self-Supervised Visual Tracking via Image Synthesis and Domain Adversarial Learning
by Gu Geng, Sida Zhou, Jianing Tang, Xinming Zhang, Qiao Liu and Di Yuan
Sensors 2025, 25(15), 4621; https://doi.org/10.3390/s25154621 - 25 Jul 2025
Viewed by 198
Abstract
With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on [...] Read more.
With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications. Full article
(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems)
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23 pages, 16115 KiB  
Article
Image Privacy Protection Communication Scheme by Fibonacci Interleaved Diffusion and Non-Degenerate Discrete Chaos
by Zhiyu Xie, Weihong Xie, Xiyuan Cheng, Zhengqin Yuan, Wenbin Cheng and Yiting Lin
Entropy 2025, 27(8), 790; https://doi.org/10.3390/e27080790 - 25 Jul 2025
Viewed by 170
Abstract
The rapid development of network communication technology has led to an increased focus on the security of image storage and transmission in multimedia information. This paper proposes an enhanced image security communication scheme based on Fibonacci interleaved diffusion and non-degenerate chaotic system to [...] Read more.
The rapid development of network communication technology has led to an increased focus on the security of image storage and transmission in multimedia information. This paper proposes an enhanced image security communication scheme based on Fibonacci interleaved diffusion and non-degenerate chaotic system to address the inadequacy of current image encryption technology. The scheme utilizes a hash function to extract the hash characteristic values of the plaintext image, generating initial perturbation keys to drive the chaotic system to generate initial pseudo-random sequences. Subsequently, the input image is subjected to a light scrambling process at the bit level. The Q matrix generated by the Fibonacci sequence is then employed to diffuse the obtained intermediate cipher image. The final ciphertext image is then generated by random direction confusion. Throughout the encryption process, plaintext correlation mechanisms are employed. Consequently, due to the feedback loop of the plaintext, this algorithm is capable of resisting known-plaintext attacks and chosen-plaintext attacks. Theoretical analysis and empirical results demonstrate that the algorithm fulfils the cryptographic requirements of confusion, diffusion, and avalanche effects, while also exhibiting a robust password space and excellent numerical statistical properties. Consequently, the security enhancement mechanism based on Fibonacci interleaved diffusion and non-degenerate chaotic system proposed in this paper effectively enhances the algorithm’s resistance to cryptographic attacks. Full article
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27 pages, 516 KiB  
Article
How Does Migrant Workers’ Return Affect Land Transfer Prices? An Investigation Based on Factor Supply–Demand Theory
by Mengfei Gao, Rui Pan and Yueqing Ji
Land 2025, 14(8), 1528; https://doi.org/10.3390/land14081528 - 24 Jul 2025
Viewed by 265
Abstract
Given the significant shifts in rural labor mobility patterns and their continuous influence on the transformation of the land factor market, it is crucial to understand the relationship between labor factor prices and land factor prices. This understanding is essential to keep land [...] Read more.
Given the significant shifts in rural labor mobility patterns and their continuous influence on the transformation of the land factor market, it is crucial to understand the relationship between labor factor prices and land factor prices. This understanding is essential to keep land factor prices within a reasonable range. This study establishes a theoretical framework to investigate how migrant workers’ return shapes land price formation mechanisms. Using 2023 micro-level survey data from eight counties in Jiangsu Province, China, this study empirically examines how migrant workers’ return affects land transfer prices and its underlying mechanisms through OLS regression and instrumental variable approaches. The findings show that under the current pattern of labor mobility, the outflow factor alone is no longer sufficient to exert substantial downward pressure on land transfer prices. Instead, the localized return of labor has emerged as a key driver behind the rise in land transfer prices. This upward mechanism is primarily realized through the following pathways. First, factor substitution effect: this effect lowers labor prices and increases the relative marginal output value of land factors. Second, supply–demand effect: migrant workers’ return simultaneously increases land demand and reduces supply, intensifying market shortages and driving up transfer prices. Lastly, the results demonstrate that enhancing the stability of land tenure security or increasing local non-agricultural employment opportunities can mitigate the effect of rising land transfer prices caused by the migrant workers’ return. According to the study’s findings, stabilizing land factor prices depends on full non-agricultural employment for migrant workers. This underscores the significance of policies that encourage employment for returning rural labor. Full article
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18 pages, 2813 KiB  
Article
Spatiotemporal Differentiation and Driving Factors Analysis of the EU Natural Gas Market Based on Geodetector
by Xin Ren, Qishen Chen, Kun Wang, Yanfei Zhang, Guodong Zheng, Chenghong Shang and Dan Song
Sustainability 2025, 17(15), 6742; https://doi.org/10.3390/su17156742 - 24 Jul 2025
Viewed by 293
Abstract
In 2022, the Russia–Ukraine conflict has severely impacted the EU’s energy supply chain, and the EU’s natural gas import pattern has begun to reconstruct, and exploring the spatiotemporal differentiation of EU natural gas trade and its driving factors is the basis for improving [...] Read more.
In 2022, the Russia–Ukraine conflict has severely impacted the EU’s energy supply chain, and the EU’s natural gas import pattern has begun to reconstruct, and exploring the spatiotemporal differentiation of EU natural gas trade and its driving factors is the basis for improving the resilience of its supply chain and ensuring the stable supply of energy resources. This paper summarizes the law of the change of its import volume by using the complex network method, constructs a multi-dimensional index system such as demand, economy, and security, and uses the geographic detector model to mine the driving factors affecting the spatiotemporal evolution of natural gas imports in EU countries and propose different sustainable development paths. The results show that from 2000 to 2023, Europe’s natural gas imports generally show an upward trend, and the import structure has undergone great changes, from pipeline gas dominance to LNG diversification. After the conflict between Russia and Ukraine, the number of import source countries has increased, the market network has become looser, France has become the core hub of the EU natural gas market, the importance of Russia has declined rapidly, and the status of countries in the United States, North Africa, and the Middle East has increased rapidly; natural gas consumption is the leading factor in the spatiotemporal differentiation of EU natural gas imports, and the influence of import distance and geopolitical risk is gradually expanding, and the proportion of energy consumption is significantly higher than that of other factors in the interaction with other factors. Combined with the driving factors, three different evolutionary directions of natural gas imports in EU countries are identified, and energy security paths such as improving supply chain control capabilities, ensuring export stability, and using location advantages to become hub nodes are proposed for different development trends. Full article
(This article belongs to the Topic Energy Economics and Sustainable Development)
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19 pages, 313 KiB  
Article
Survey on the Role of Mechanistic Interpretability in Generative AI
by Leonardo Ranaldi
Big Data Cogn. Comput. 2025, 9(8), 193; https://doi.org/10.3390/bdcc9080193 - 23 Jul 2025
Viewed by 755
Abstract
The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive [...] Read more.
The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive modelling. These developments have led to the emergence of increasingly sophisticated models capable of learning patterns, reasoning over complex data structures, and generalising across tasks. As AI systems become more deeply integrated into networked infrastructures and the Internet of Things (IoT), their ability to process and interpret data in real-time is essential for optimising intelligent communication networks, distributed decision making, and autonomous IoT systems. However, despite these achievements, the internal mechanisms that drive LLMs’ reasoning and generalisation capabilities remain largely unexplored. This lack of transparency, compounded by challenges such as hallucinations, adversarial perturbations, and misaligned human expectations, raises concerns about their safe and beneficial deployment. Understanding the underlying principles governing AI models is crucial for their integration into intelligent network systems, automated decision-making processes, and secure digital infrastructures. This paper provides a comprehensive analysis of explainability approaches aimed at uncovering the fundamental mechanisms of LLMs. We investigate the strategic components contributing to their generalisation abilities, focusing on methods to quantify acquired knowledge and assess its representation within model parameters. Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge is structured, encoded, and retrieved in AI systems. Furthermore, by adopting a mechanistic perspective, we analyse emergent phenomena within training dynamics, particularly memorisation and generalisation, which also play a crucial role in broader AI-driven systems, including adaptive network intelligence, edge computing, and real-time decision-making architectures. Understanding these principles is crucial for bridging the gap between black-box AI models and practical, explainable AI applications, thereby ensuring trust, robustness, and efficiency in language-based and general AI systems. Full article
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37 pages, 55522 KiB  
Article
EPCNet: Implementing an ‘Artificial Fovea’ for More Efficient Monitoring Using the Sensor Fusion of an Event-Based and a Frame-Based Camera
by Orla Sealy Phelan, Dara Molloy, Roshan George, Edward Jones, Martin Glavin and Brian Deegan
Sensors 2025, 25(15), 4540; https://doi.org/10.3390/s25154540 - 22 Jul 2025
Viewed by 234
Abstract
Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional [...] Read more.
Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article
(This article belongs to the Section Sensing and Imaging)
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28 pages, 2012 KiB  
Article
The Convergence of Trafficking and Migrant Smuggling in West Africa: Migration Pressure Factors and Criminal Actors
by Concepción Anguita-Olmedo
Soc. Sci. 2025, 14(8), 447; https://doi.org/10.3390/socsci14080447 - 22 Jul 2025
Viewed by 529
Abstract
In West Africa, there is a very close link between the phenomenon of trafficking and migrant smuggling. This article will analyze the pressure elements and the causes that drive sub-Saharan people to migrate, placing themselves in the hands of criminal networks that end [...] Read more.
In West Africa, there is a very close link between the phenomenon of trafficking and migrant smuggling. This article will analyze the pressure elements and the causes that drive sub-Saharan people to migrate, placing themselves in the hands of criminal networks that end up exploiting them—women and minors sexually, and men through forced labor. The main corridors departing from West Africa and the characteristics of the criminal groups exercising criminal governance will also be addressed. This research has used both primary and secondary sources, as well as empirical fieldwork consisting of interviews with security force officials, international humanitarian aid organizations, and academic experts on migration issues related to trafficking and smuggling. Our research reveals that the origin of migration is multifactorial. The violence experienced in West Africa, but also the misgovernance, the lack of opportunities for a very young population with limited prospects, and the human insecurity affecting the entire region, are the main reasons that compel people to migrate. In these migration processes, the safety of migrants is compromised as they are forced to start their journey through clandestine means, which exposes them to trafficking networks and thus to violence and exploitation. It is along the migration routes where trafficking and migrant smuggling converge. Full article
(This article belongs to the Collection Tackling Organized Crime and Human Trafficking)
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15 pages, 1006 KiB  
Review
Multifunctional Applications of Biofloc Technology (BFT) in Sustainable Aquaculture: A Review
by Changwei Li and Limin Dai
Fishes 2025, 10(7), 353; https://doi.org/10.3390/fishes10070353 - 17 Jul 2025
Viewed by 378
Abstract
Biofloc technology (BFT), traditionally centered on feed supplementation and water purification in aquaculture, harbors untapped multifunctional potential as a sustainable resource management platform. This review systematically explores beyond conventional applications. BFT leverages microbial consortia to drive resource recovery, yielding bioactive compounds with antibacterial/antioxidant [...] Read more.
Biofloc technology (BFT), traditionally centered on feed supplementation and water purification in aquaculture, harbors untapped multifunctional potential as a sustainable resource management platform. This review systematically explores beyond conventional applications. BFT leverages microbial consortia to drive resource recovery, yielding bioactive compounds with antibacterial/antioxidant properties, microbial proteins for efficient feed production, and algae biomass for nutrient recycling and bioenergy. In environmental remediation, its porous microbial aggregates remove microplastics and heavy metals through integrated physical, chemical, and biological mechanisms, addressing critical aquatic pollution challenges. Agri-aquatic integration systems create symbiotic loops where nutrient-rich aquaculture effluents fertilize plant cultures, while plants act as natural filters to stabilize water quality, reducing freshwater dependence and enhancing resource efficiency. Emerging applications, including pigment extraction for ornamental fish and the anaerobic fermentation of biofloc waste into organic amendments, further demonstrate its alignment with circular economy principles. While technical advancements highlight its capacity to balance productivity and ecological stewardship, challenges in large-scale optimization, long-term system stability, and economic viability necessitate interdisciplinary research. By shifting focus to its underexplored functionalities, this review positions BFT as a transformative technology capable of addressing interconnected global challenges in food security, pollution mitigation, and sustainable resource use, offering a scalable framework for the future of aquaculture and beyond. Full article
(This article belongs to the Section Sustainable Aquaculture)
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