Search Results (3,825)

This paper introduces a novel similarity measure to link transactions which spend outputs of CoinJoin transactions, CoinJoin Spending Transactions (CSTs), by analyzing their on-chain properties, addressing the challenge of preserving user privacy in blockchain systems. Despite the adoption of privacy-enhancing techniques like CoinJoin, users remain vulnerable to transaction linkage through shared output patterns. The proposed method leverages timestamp analysis of mixed outputs and employs a one-sided Chamfer distance to quantify similarities between CSTs, enabling the identification of transactions associated with the same user. The approach is evaluated across three major CoinJoin implementations (Dash, Whirlpool, and Wasabi 2.0) demonstrating its effectiveness in detecting linked CSTs. Additionally, the work improves transaction classification rules for Wasabi 2.0 by introducing criteria for uncommon denomination outputs, reducing false positives. Results show that multiple CSTs spending shared CoinJoin outputs are prevalent, highlighting the practical significance of the similarity measure. The findings underscore the ongoing privacy risks posed by transaction linkage, even within privacy-focused protocols. This work contributes to the understanding of CoinJoin’s limitations and offers insights for developing more robust privacy mechanisms in decentralized systems. To the authors knowledge this is the first work analyzing the linkage between CSTs. Full article

The purpose of this research is to systematically map and analyze the use of AI technologies in project management, identifying themes, research gaps, and practical implications. This study conducts a systematic literature review (SLR) that combines bibliometric analysis with qualitative content evaluation to explore the present landscape of AI in project management. The search covered literature published until November 2024, ensuring inclusion of the most recent developments. Studies were included if they examined AI methods applied to project management contexts and were published in peer-reviewed English journals as articles, review articles, or early access publications; studies unrelated to project management or lacking methodological clarity were excluded. It follows a structured coding protocol informed by inductive and deductive reasoning, using NVivo (version 12) and Biblioshiny (version 4.3.0) software. From the entire set of 1064 records retrieved from Scopus and Web of Science, 27 publications met the final inclusion criteria for qualitative synthesis. Bibliometric clusters were derived from the entire set of 885 screened records, while thematic coding was applied to the 27 included studies. This review highlights the use of Artificial Neural Networks (ANN), Case-Based Reasoning (CBR), Digital Twins (DTs), and Large Language Models (LLMs) as central to recent progress. Bibliometric mapping identified several major thematic clusters. For this study, we chose those that show a clear link between artificial intelligence (AI) and project management (PM), such as expert systems, intelligent systems, and optimization algorithms. These clusters highlight the increasing influence of AI in improving project planning, decision-making, and resource management. Further studies investigate generative AI and the convergence of AI with blockchain and Internet of Things (IoT) systems, suggesting changes in project delivery approaches. Although adoption is increasing, key implementation issues persist. These include limited empirical evidence, inadequate attention to later project stages, and concerns about data quality, transparency, and workforce adaptation. This review improves understanding of AI’s role in project contexts and outlines areas for further research. For practitioners, the findings emphasize AI’s ability in cost prediction, scheduling, and risk assessment, while also emphasizing the importance of strong data governance and workforce training. This review is limited to English-language, peer-reviewed research indexed in Scopus and Web of Science, potentially excluding relevant grey literature or non-English contributions. This review was not registered and received no external funding. Full article

Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems. Full article

Heavy metals (HMs) contamination in agricultural soils poses significant risks to crop production and human health through bioaccumulation in the food chain. While traditional remediation techniques exist, they often face limitations including high operational costs, low efficiency, and time-intensive processes. Nano-enabled agrochemicals have emerged as a promising solution for HM remediation in contaminated soils. In this review, we highlight distinct nano-enabled mechanisms involved in HMs remediation in agricultural soils. Further, this review describes HM remediation potential of three different classes of nano-agrochemicals exhibiting unique physicochemical properties, such as surface charge, controlled release capability, and metal chelating ability, etc. Nano-agrochemicals also enhance plant resilience through multiple pathways, such as the regulation of nutrient profiles and photosynthesis, activation of antioxidant defense systems, modulation of protein and osmolyte synthesis, stimulation of phytohormone pathways, and activation of stress-responsive transcription factors. While nano-agrochemicals show tremendous potential for sustainable agriculture, their environmental impact and safety considerations require careful assessment. The review highlights the need for continued research to fully understand nano-agrochemical interactions with plants and soil ecosystems, and to develop improved strategies for their safe and effective implementation in agricultural systems. Future studies should focus on optimizing nano-agrochemical formulations, investigating long-term effects, and establishing comprehensive risk assessment frameworks. Full article

The compounding effects of hydrometeorological hazards are being driven by climate change. As urban areas expand, this leads to degradation of the surrounding environment and exposes more people to hazards. Growing losses show that conventional approaches to addressing these issues can compound these problems. Over the last few decades, nature-based solutions (NBSs) have become an increasingly popular alternative. These measures, inspired by natural processes, have shown potential for reducing hazards by complementing traditional approaches and providing co-benefits in the form of eco-system services. With the adoption of NBSs becoming a more mainstream approach, there is a need for tools that support the planning and implementation of interventions. Geospatial suitability assessment is a part of this planning process. Existing tools are limited in their application for large-scale measures. This paper intends to improve this by building upon a multi-criteria analysis (MCA)-based approach that incorporates biophysical and land use criteria and conditions for mapping the suitability of large-scale NBSs. The methodology was developed and tested on six sites to assess the suitability of floodplain restoration, retention or detention, afforestation, and forest buffer strips. The resulting suitability maps also show potential for combining two or more measures for greater risk reduction. Full article

In a botnet attack, significant damage can occur when an attacker gains control over a large number of compromised network devices. Botnets have evolved from traditional centralized architectures to decentralized Peer-to-Peer (P2P) and hybrid forms. Recently, a steganography-based botnet (Stego-botnet) has emerged, which conceals command and control (C&C) messages within cover media such as images or video files shared over social networking sites (SNS). This type of Stego-botnet can evade conventional detection systems, as identifying hidden messages embedded in media transmitted via SNS platforms is inherently challenging. However, the inherent file size limitations of SNS platforms restrict the achievable payload capacity of such Stego-botnets. Moreover, the centralized characteristics of conventional botnet architectures expose attackers to a higher risk of identification. To overcome these challenges, researchers have explored network steganography techniques leveraging P2P networks such as BitTorrent, Google Suggest, and Skype. Among these, a hidden communication method utilizing Bitfield messages in BitTorrent has been proposed, demonstrating improved concealment compared to prior studies. Nevertheless, existing approaches still fail to achieve sufficient payload capacity relative to traditional digital steganography techniques. In this study, we extend P2P-based network steganography methods—particularly within the BitTorrent protocol—to address these limitations. We propose a novel botnet C&C communication model that employs network steganography over BitTorrent and validate its feasibility through experimental implementation. Furthermore, our results show that the proposed Stego-botnet achieves a higher payload capacity and outperforms existing Stego-botnet models in terms of both efficiency and concealment performance. Full article

Background: Routine early surgery for all deep burns in low-resource settings is not currently achievable. We designed and implemented a simple triage strategy that selected patients to be prioritised for early surgery based on a more urgent need and greater potential benefit. The primary outcome was the ability to perform surgery in the priority group within three days of the decision. Methods: This was a prospective, descriptive study undertaken at a tertiary hospital in Pietermaritzburg, South Africa. All patients referred to the Grey’s Hospital Burn Service were triaged into either priority or non-priority groups. Priority designation was based on total burn surface area (TBSA) > 15%, the presence of sepsis, or limb-threatening injury. Data related to demographic information, injury, and outcomes were collected and managed using REDCap electronic data capture tools. Results: There were 191 admissions with 42 (22%) meeting priority criteria. The priority group had larger burns (TBSA 25 [Interquartile range 15–30] vs. 8 [3–15]%) and included all septic injuries. We provided early surgery within a median of 1.4 (interquartile range 0.5–3.3) days of the decision for surgery being made. A total of 75% of patients were operated within 72 h of the decision, and 43% within 10 days of injury. The system identified a sicker cohort, as evidenced by high mortality, ICU admission, and acute kidney injury rates. In the non-priority group, reported outcomes were more positive, but with a high injury-to-discharge days per percentage TBSA. Conclusions: This simple triage strategy represents a novel approach for prioritising access to burn surgery in a setting where global surgery standards are desirable but not always possible. We were able to identify the high-risk groups and provide surgery within acceptable time frames. Future research should be aimed at refining this triage system and improving outcomes in the priority group. Full article

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

Heirs’ property is a form of collective land ownership arising from intestate succession, often resulting in clouded titles, fractional ownership, and legal vulnerability. This tenure system is especially prevalent among African American landowners in the Southern United States and poses significant challenges for sustainable land use, agricultural development, forest management, and conservation. This paper presents an interdisciplinary review, research, and analysis encompassing legal studies, environmental policies, and rural social science to examine how heirs’ property status leads to diminished productivity, land underutilization, disinvestment, and involuntary land loss. Key issues include barriers to accessing USDA and NRCS programs, an inability to implement long-term land management plans, and an increased risk of partition sales and tax foreclosures. This review also examines demographic trends, regional concentration, and the broader socio-environmental impacts of insecure land tenure. Current policy responses, such as the Uniform Partition of Heirs Property Act (UPHPA), USDA land access provisions, and community-based legal interventions, are assessed for their effectiveness and limitations. The article concludes with policy and programmatic strategies to support title clearing, promote equitable land retention, and enhance participation in conservation and climate resilience initiatives. By highlighting the intersection of property law, racial equity, and environmental sustainability, this review contributes to a growing body of research aimed at securing land tenure for historically marginalized communities. Full article

Background: Older adults are highly vulnerable to infectious diseases due to immunosenescence, multimorbidity, and atypical presentations. Artificial intelligence (AI) offers promising opportunities to improve diagnosis, prognosis, treatment, and continuity of care in this population. This review summarizes current applications of AI in the management of infections in older adults across diagnostic, prognostic, therapeutic, and preventive domains. Methods: We conducted a narrative review of peer-reviewed studies retrieved from PubMed, Scopus, and Web of Science, focusing on AI-based tools for infection diagnosis, risk prediction, antimicrobial stewardship, prevention of healthcare-associated infections, and post-discharge care in individuals aged ≥65 years. Results: AI models, including machine learning, deep learning, and natural language processing techniques, have demonstrated high performance in detecting infections such as sepsis, pneumonia, and healthcare-associated infections (Area Under the Curve AUC up to 0.98). Prognostic algorithms integrating frailty and functional status enhance the prediction of mortality, complications, and readmission. AI-driven clinical decision support systems contribute to optimized antimicrobial therapy and timely interventions, while remote monitoring and telemedicine applications support safer hospital-to-home transitions and reduced 30-day readmissions. However, the implementation of these technologies is limited by the underrepresentation of frail older adults in training datasets, lack of real-world validation in geriatric settings, and the insufficient explainability of many models. Additional barriers include system interoperability issues and variable digital infrastructure, particularly in long-term care and community settings. Conclusions: AI has strong potential to support predictive and personalized infection management in older adults. Future research should focus on developing geriatric-specific, interpretable models, improving system integration, and fostering interdisciplinary collaboration to ensure safe and equitable implementation. Full article

Industrial safety in high-risk sectors such as mining, construction, oil and gas, petrochemicals, and offshore fishing remains a strategic global challenge due to the high incidence of occupational accidents and their human, financial, and legal consequences. Despite international standards and advancements in safety strategies, significant barriers persist in the effective implementation of a Zero Accident culture. This scoping review, conducted under PRISMA-ScR guidelines, analyzed 11 studies selected from 232 records, focusing on documented practices in both multinational corporations from developed economies and local companies in emerging markets. The methodological synthesis validated theoretical models, practical interventions, and regulatory frameworks across diverse industrial settings. The findings led to the construction of a five-pillar model that provides the structural foundation for a comprehensive safety strategy: (1) strategic safety planning, defining long-term vision, mission, and objectives with systematic risk analysis; (2) executive leadership and commitment, expressed through decision-making, resource allocation, and on-site engagement; (3) people and competencies, emphasizing continuous training, communities of practice, and the development of safe behaviors; (4) process risk management, using validated protocols, structured methodologies, and early warning systems; and (5) performance measurement and auditing, combining reactive and proactive indicators within continuous improvement cycles. The results demonstrate that only a holistic approach, one that aligns strategy, culture, and performance, can sustain a robust safety culture. While notable reductions in incident rates were observed when these pillars were applied, the current literature is dominated by theoretical contributions and model replication from developed countries, with limited empirical evaluation in emerging contexts. This study provides a comparative, practice-oriented framework to guide the implementation and refinement of safety systems in high-risk organizations. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/XFDPR. Full article

Background: Colorectal cancer (CRC) represents one of the leading causes of cancer-related morbidity and mortality globally. Although national screening programs in Europe and the United States have demonstrated success in reducing incidence and death rates among populations aged 50 and above, a concerning increase in early-onset colorectal cancer (EOCRC), defined as diagnosis before age 50, has emerged. Methods: This paper is a narrative literature review comparing American and European CRC screening guidelines. A comprehensive search was conducted using the PubMed database with emphasis on publications from the past ten years. Results: The United States has adapted more swiftly to EOCRC trends by lowering the recommended screening age to 45, supported by modeling studies showing life-years gained and improved cost-effectiveness. In contrast, European programs remain largely organized and cost-efficient but predominantly initiate screening at age 50, potentially missing high-risk younger adults. EOCRC appears to demonstrate unique molecular and pathological features compared to late-onset CRC. Participation and adherence to screening also vary significantly between regions and modalities, with colonoscopy remaining the gold standard but less scalable than fecal immunochemical tests. Conclusions: The rising incidence of EOCRC calls for a reassessment of CRC screening policies. While the European model emphasizes equity and structure, its slower responsiveness to epidemiological changes may lead to late detection in younger cohorts. The American model’s earlier screening age addresses emerging trends but faces challenges in implementation equity. A hybrid approach may provide the optimal management, balancing public health benefit with system sustainability. Full article

Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the attack surface, exposing RTOSs to a variety of security threats, including memory corruption, privilege escalation, and side-channel attacks. Traditional security mechanisms often impose additional overhead that can compromise real-time guarantees. In this work, we present a Risk-aware Permission Adjustment (RPA) framework, implemented on CHERIoT RTOS, which is a CHERI-based operating system. RPA aims to detect anomalous behavior in real time, quantify security risks, and dynamically adjust permissions to mitigate potential threats. RPA maintains system continuity, enforces fine-grained access control, and progressively contains the impact of violations without interrupting critical operations. The framework was evaluated through targeted fault injection experiments, including 20 real-world CVEs and 15 abstract vulnerability classes, demonstrating its ability to mitigate both known and generalized attacks. Performance measurements indicate minimal runtime overhead while significantly reducing system downtime compared to conventional CHERIoT and FreeRTOS implementations. Full article

Coastal areas face unique challenges, with climate change impacts such as sea level rise exacerbating coastal hazards that put people, infrastructure, and habitats at risk. This study evaluates three generations of Shoreline Management Plans (SMPs) in the UK (1993–2024). The study considers whether the system of governance is encouraging sustainable solutions. Policy analysis identifies a range of sustainability principles which have become dominant for SMPs. Findings show that shoreline management planning has evolved considerably over the last 30 years. It has transformed governance from a reactive, engineering-focused, administratively based approach to a risk-based, geostrategic, technically informed approach. SMPs have slowed the increase of coastal vulnerability. In the most recent phase, they have increased consideration of adaptation to the impacts of climatic change. However, strategic goals are not always translated into locally implemented action, because of problems with criteria, collaboration, costs, cultural attitudes, competing priorities for coastal landuse, and contentious decisions, especially those which set public interest against individual interests. So, governing the shoreface will need to evolve further to deal with the tensions between ‘working with nature’, ‘working in partnership with people’, and ‘adapting to future climates’. Full article

Background and Objectives: Harmful repetitive oral behaviors impose an excessive load on the stomatognathic system. Being unconscious and involuntary, patients are often unaware of their occurrence and, consequently, of their potential consequences. We aimed to screen the Romanian population for harmful oral habits, while simultaneously emphasizing the importance of employing validated and internationally accepted diagnostic instruments for a better approach to these conditions. Materials and Methods: An observational, descriptive study was conducted on 459 adults, recruited through a multiregional convenience sampling from the general population in Romania. Oral behaviors were assessed using the validated Oral Behaviors Checklist (OBC-21) questionnaire. Data was analyzed using descriptive and comparative statistics, with significance set at p < 0.05. Results: The sample included 363 women (79.1%) and 96 men (20.9%), with a mean age of 33.3 years. The mean total OBC score was 22.45 ± 10.27, indicating a moderate prevalence of oral behaviors. 60.1% of participants were classified as low-risk and 39.9% as high-risk, with none in the no-risk category. The most frequently reported behaviors were sleeping positions exerting pressure on the mandible (57.7% “very often”), sustained talking (11.3%), and nocturnal bruxism (10.5%). Younger adults (20–49 years) presented significantly higher OBC scores compared to both younger extremes (18 years) and older adults (>60 years) (p < 0.001). No significant gender differences were observed in total OBC scores; however, unilateral chewing, sustained talking, and holding objects between the teeth were significantly more frequent among women (p < 0.05). Conclusions: This is the first study to investigate oral behaviors in a Romanian adult population. Postural and involuntary activities were the most prevalent and age influenced OBC scores, while gender differences were limited to individual behaviors. Conducting screening and implementing therapeutic interventions based on the assessed level of risk could enhance the overall management of the condition. Full article