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History of a Revoluter: The Life of James Leslie Mitchell/Lewis Grassic Gibbon. By William K. Malcolm
No full textAI-assisted data extraction helps uncover spatiotemporal patterns and socioeconomic drivers of wildlife crime involving sea turtles
No full textCrimes associated with an ever-increasing demand for wildlife products are one of the most notable threats to marine and freshwater ecosystems. To combat such crimes, it is crucial to identify their spatiotemporal patterns and hotspots, which have largely been overlooked in previous research. However, especially after the emergence of large language models (LLMs), this process has become more time-efficient and accurate. In this study, we analyzed spatiotemporal patterns and socioeconomic drivers of wildlife crime in sea turtles, using Deepseek to extract data from 247 court verdicts. DeepSeek data extraction reached an accuracy of over 99 % in extracting 25 items from each verdict. We found that most individual sea turtles and products were seized in southeastern coastal cities of China and identified two main trafficking hotspots. First, nearly 73 % (450/613) of the hawksbill turtles and 84 % (325/386) of the green turtles were seized or originated from Hainan province, China. Second, nearly 98 % (207/211) of the loggerhead turtles were seized from Zhoushan, Zhejiang province. Moreover, nearly all the manufactured products (over 99 %, mainly made of tortoiseshell) were seized or originated from Hainan. Destinations of trafficking tended to be northern inland cities, with one main hotspot: 5.5 % (68/1236) of individuals and 30.3 % (8896/29,323) of the products were seized in Xuzhou, Jiangsu province, which originated from Hainan. Our study highlights how AI tools can boost biodiversity conservation research by leveraging large datasets. In doing so, we were able to identify major hotspots of wildlife crime, as well as main trafficking routes. These findings might be relevant for law enforcement efforts and help to enhance sea turtle conservation
Public Consultation Response Submitted to Prostitution (Offences and Support) (Scotland) Bill
No full textComparative analysis of sustainable nanocomposite cellulose nanofiber membranes with polycaprolactone, and polylactic acid
Get PDFThis study presents sustainable membranes prepared from cellulose nanofibers (CNF) using a simple solution-casting method, focusing on the incorporation of low weight percentages (1 %, 2 %, and 4 %) of polycaprolactone (PCL) and polylactic acid (PLA) to compare membrane performance. The nanocomposite membranes were evaluated based on thickness, wettability, electrolyte uptake, porosity, thermal behaviour, and mechanical properties. The results indicate that CNF-PCL membranes exhibit superior mechanical flexibility and stress tolerance but lower thermal stability, with a glass transition temperature (Tg) of 34.39 °C at 1 wt%, compared to CNF-PLA’s Tg of 144.15 °C at the same concentration. The higher crystallinity and greater hydrophilicity of PLA enhance its stability. Additionally, CNF-PLA membranes demonstrate better interfacial compatibility due to hydrogen bonding between PLA’s ester linkages and cellulose’s hydroxyl groups, improving dispersion, liquid uptake, and overall hydrophilicity (34.66° for CNF-PLA vs. 72.64° for CNF-PCL at 1 wt% loading). These properties make CNF-PLA membranes more resistant to plasticisation and better suited for high-temperature applications. These findings highlight the crucial role of polymer selection and concentration in optimising CNF-based membranes for specific applications
MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing
Get PDFWith the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study proposes a lightweight deep learning model, MMST-LSTM, which integrates Multiscale Context Feature Fusion Mechanism (MCFFM) and Mixed-Domain Attention Fusion Mechanism (MAFUM). While maintaining high prediction accuracy, MMST-LSTM significantly improves forecast speed. The MMST-LSTM model is particularly suitable for deployment in Mobile Edge Computing (MEC) environments, enabling fast localized forecasting. Experimental results demonstrate MMST-LSTM’s excellent predictive performance on two radar echo datasets, particularly in rapid response and handling localized data. Moreover, leveraging Smart Data-Driven Modeling (SDDM) technology with consumer-generated data enhances its application potential in smart consumer electronics products, providing an efficient tool for disaster weather alerts. This study introduces an innovative meteorological forecasting method and provides robust technical support for accurate weather warning systems, offering consumers timely and reliable weather information. This enables them to make more informed decisions, effectively reducing the potential risks and economic losses caused by extreme climate events
Arabic Cyberbullying Detection: A Comprehensive Review of Datasets and Methodologies
Get PDFThe freedom of speech in online spaces has substantially promoted engagement on social media platforms, where cyberbullying has emerged as a significant consequence. While extensive research has been conducted on cyberbullying detection in English, efforts in the Arabic language remain limited. To address this gap, the current study provides a comprehensive, state-of-the-art review of datasets and methodologies specifically focused on Arabic cyberbullying detection. It systematically reviews different relevant studies from six academic databases, examining their methodologies, dataset characteristics, and performance in terms of classification accuracy and limitations. The paper critically evaluates existing Arabic cyberbullying datasets according to criteria such as dataset size, dialectal diversity, annotation processes, and accessibility. Additionally, this review identifies critical limitations, including dataset scarcity, dialectal imbalance, annotation subjectivity, and methodological constraints. By synthesizing current knowledge, identifying research gaps, and suggesting future directions, this review supports the development of more robust, effective, and linguistically inclusive analytical methods. Ultimately, this work contributes significantly to natural language processing research and advances the creation of safer online environments for Arabic-speaking users
Corporate Climate Risk and Membership of Emission Trading Schemes
Get PDFUsing a sample of 5364 firms from 65 countries, we demonstrate that membership in the scheme increases firm climate risk. Further analysis reveals that the positive impact of membership on climate risk is pronounced among firms in carbon-intensive industries. Our findings demonstrate that continental differences and legal origin could moderate or exacerbate the relationship between emission trading schemes (ETSs) and corporate climate risk. Similarly, the positive relationship between ETSs and corporate climate risk is only significant in the period after the Paris Agreement. This indicates that public interest in climate change discussions may have driven membership in the initiative rather than reflecting a real commitment to reducing carbon emissions. Additionally, we show that membership has short- to medium-term effects on corporate climate risk. Our results are robust to a battery of tests such as propensity score matching (PSM) and generalized method of moments (GMM)
Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach
Get PDFThe Distributed Network Protocol 3 (DNP3) protocols focus on securing critical infrastructure communication in sectors such as energy and supervisory control and data acquisition (SCADA) systems. The security of DNP3 is paramount, employing features such as encryption, authentication, and secure key management to safeguard against cyber threats. The robust security framework ensures the reliability and integrity of data exchange, fortifying the resilience of industrial control systems against potential cyber-attacks. This study investigates Smart Grid (SG) DNP3 communication security and provides a deep learning-driven approach to detect and prevent cyber-attacks in the SG. Securing communication in SG is a critical challenge, particularly for protocols such as DNP3, which is essential to SCADA systems. This study explores the potential for enhancing intrusion detection in DNP3 communications and the associated industrial control system traffic through the application of state-of-the-art deep learning (DL) algorithms. A Segmented Neural Network (SNN) architecture is employed to analyze the DNP3 dataset, which is captured using CICFlowMeter3 and a DNP3 Parser, integrating Deep Neural Network (DNN), Long Short Term Memory (LSTM), and Random Neural Network (RandNN) models. In CICFlowMeter3, the model achieved an accuracy of 99.86%, whereas, on the DNP3 Parser, it improved to 99.75%, demonstrating outstanding performance. These findings show that the proposed framework is efficient and resilient with complicated and varied data streams. The results show that the proposed SNN-based solution improved the security and resilience of SG operations to detect anomalies in industrial control systems (ICS) in real-time
Foot Pressure-Based Abnormal Gait Recognition With Multi-Scale Cross-Attention Fusion
Get PDFAbnormal gait recognition plays a critical role in healthcare, particularly for the early diagnosis and continuous monitoring of neurological and musculoskeletal disorders, such as Parkinson’s disease and orthopedic injuries. This study proposes MSCAF-Gait, a Multi-Scale Cross-Attention Fusion Network designed specifically for abnormal gait recognition using foot pressure sensors. MSCAF-Gait incorporates multi-scale convolutional modules with channel and spatial attention mechanisms to effectively capture features across temporal, channel, and spatial dimensions. A novel cross-attention fusion module further enhances feature representation, enabling precise recognition of diverse abnormal gait patterns. To facilitate this research, we introduce the Pressure-Insole Abnormal Gait (PIAG) dataset, comprising gait data associated with common neurological and musculoskeletal abnormalities. Extensive experiments on the publicly available Gait in Parkinson’s Disease (GaitinPD) dataset and our self-constructed PIAG dataset validate the effectiveness of MSCAF-Gait. Specifically, the model achieves 99.61% accuracy in Parkinsonian gait recognition and 98.88% accuracy in Parkinson’s severity classification. On the PIAG dataset, which includes multiple abnormal gait patterns, MSCAF-Gait attains a high accuracy of 99.42%. Notably, these results are obtained with a lightweight architecture characterized by reduced FLOPs and parameter count, demonstrating that MSCAF-Gait offers both high accuracy and computational efficiency, making it well-suited for real-time deployment on wearable platforms
Climate governance, CSR strategy, and corporate environmental decoupling
Get PDFThis study, utilizing a global dataset of 32,382 firm-year observations from 2004 to 2023, reveals a positive and statistically significant relationship between climate governance and corporate environmental decoupling. We find that climate governance decreases under-reporting (corporations disclose fewer details about their sustainability practices than they perform) and increases over-reporting (corporations report sustainability practices more extensively than they perform). However, the mere presence of climate governance is insufficient. The study contributes to the literature by confirming that when combined with a strong CSR strategy and third-party assurance (e.g., Big 4 audits, external CSR audits), climate governance becomes more effective in mitigating overall corporate environmental decoupling. The study offers important implications for corporate boards and policymakers seeking to align sustainability disclosures with actual performance
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