Search Results (52)

Underwater cultural heritage (UCH) is a valuable resource that preserves humanity’s historical legacy, offering insights into traditions and civilisations. Despite its significance, UCH faces threats from inadequate regulatory frameworks, insufficient conservation technologies, and climate-induced environmental changes. This paper proposes an innovative platform combining the internet of underwater things and edge computing technologies to enhance UCH’s real-time monitoring, localisation, and management. The platform processes data through a central unit installed on a buoy near heritage sites, enabling efficient data analysis and decision making without relying on cloud connectivity. Integrating acoustic communication systems, LoRa technology, and nonterrestrial networks supports a robust multilayered communication infrastructure for continuous operation, even in remote maritime areas. The platform’s edge node deploys artificial intelligence models for real-time risk assessment, focusing on key environmental parameters to predict and mitigate corrosion rates and climate-related threats. A case study illustrates the system’s capabilities in underwater localisation, demonstrating how edge computing and acoustic triangulation techniques enable precise tracking. Full article

An ecological understanding of threatened species provides the basis for their protection and recovery. This information must be used to analyze threats in order to propose conservation strategies for target species. River management projects, such as the construction of dikes, revetments, and dredging, are often undertaken to prevent flooding, and these activities affect fish communities and population dynamics. The critically endangered Pseudobagrus brevicorpus is highly vulnerable, but the causes of its decline are poorly understood. In this study, we assess the movements and habitat selection of P. brevicorpus to better understand its ecological characteristics and analyse the causes of its decline. We used radio telemetry to track the movements of the species and compared the effects of river-maintenance projects with data from a long-term study of the distribution of this endangered species. Total movements and home ranges were quite limited, with an average total distance traveled of 107.58 ± 66.01 m over an approximately 8-week monitoring period. The average MCP (minimum convex polygon) was 341.91 ± 776.35 m², the KDE (kernel density estimation) 50 was 76.01 ± 30.98 m², and the KDE 95 was 144.41 ± 58.86 m². The species is nocturnal, and during the day, individuals primarily hide among rocks and aquatic roots. The movement and habitat selection of P. brevicorpus indicated that the species could be directly or indirectly affected by river management. Acute population declines have been anticipated due to a lack of avoidance during management, and post-management habitat loss appears to have contributed to long-term population declines. Therefore, a strategic approach that considers ecological consequences is urgently needed to prevent the extinction of this species. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article

Climate change poses significant threats to coffee supply chains, highlighting the crucial role of coffee genetic resources in enhancing resilience and improving the livelihoods of coffee farmers. Increasing climate change effects are intensifying pressure to develop new high-performance resilient varieties. Current cultivated coffee species include Coffea arabica and C. canephora, while uncultivated genetic resources include C. stenophylla, C. racemosa, and many others among the 130 known coffee species. To protect and recognize the property rights of countries and people hosting and conserving genetic resources, the international community has developed regulations embodied in the Plant Treaty and the Nagoya Protocol, among others. The majority of coffee genetic resources originate in Africa and are maintained in large field collections, particularly in Côte d’Ivoire, Ethiopia, and Madagascar. The 2023 International Coffee Convention (ICC) highlighted the need for community awareness in applying these international regulations. To foster a common understanding and establish precise rules for exchanging coffee genetic resources, the Crop Trust and the International Coffee Organization organized an invitation-only satellite workshop in Mannheim, Germany, on 16 October 2024, in connection with ICC 2024. International experts on the Nagoya Protocol and Plant Treaty and genebank experts were invited to participate. This presentation summarizes key outcomes from the workshop, covering topics such as (i) key requirements of the Convention on Biological Diversity (CBD), its Nagoya Protocol, and the Plant Treaty specifically applicable to the coffee sector; (ii) assessment of the coffee sector’s readiness to implement these international regulations for the transparent use and exchange of coffee genetic resources; (iii) suggestions for mechanisms enabling transparent use and exchange of coffee genetic resources in compliance with international regulations; (iv) evaluation of strategies for generating benefits for communities hosting coffee genetic resources; (v) a practical, user-friendly checklist to ensure the correct handling of coffee genetic resources in line with international regulations; and (vi) a practical decision-making tree with examples to differentiate genetic resources falling under Nagoya/CBD and the Plant Treaty from others. The workshop’s discussions and outcomes expanded on these topics, yielding several concrete initiatives and recommendations. Most importantly, the workshop identified critical gaps in existing coffee genetic resource collections and proposed a global safety duplication strategy. Participants conceptualized a global platform to facilitate the exchange and use of coffee genetic resources, including a centralized database and a system for tracking benefit-sharing obligations. A comprehensive list categorizing coffee varieties based on their status under the Nagoya Protocol may be initiated to clarify access and benefit-sharing requirements. The workshop concluded with a clear roadmap for advancing coffee genetic resource conservation and exchange. Full article

Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated bird detection and recognition have become possible. This study aimed to further advance the task of detecting wild birds using computer vision methods with drone footage, as well as entirely automating the process of detection and tracking. However, detecting objects from drone footage presents a significant challenge, due to the elevated altitudes, as well as the dynamic movement of both the drone and the birds. In this study, we developed and introduce a state-of-the-art model titled ORACLE (optimized rigorous advanced cutting-edge model for leveraging protection to ecosystems). ORACLE aims to facilitate robust communication across multiple models, with the goal of data retrieval, rigorously using various computer vision techniques such as object detection and multi-object tracking (MOT). The results of ORACLE’s vision models were evaluated at 91.89% mAP at 50% IoU. Full article

Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment. Full article

Sea turtles are keystone species in marine environments due to their essential role as seagrass grazers and population regulation of jellyfish and sponges in coral reefs. However, due to their predominant presence in coastal areas, sea turtle populations face significant threats due to the impact of human activities. In this systematic review, 655 peer-reviewed publications were analyzed to assess the extent of population monitoring for all seven sea turtle species. The analyses revealed that, although population monitoring studies have increased for sea turtles in the past four decades, these have been biased towards certain species and oceanic regions. Furthermore, sea turtle population monitoring has been undertaken primarily using field-based methods, with satellite tracking and nest surveys being the most commonly used methods; however, the implementation of genetic methods for population monitoring has increased since the 2000s. Direct conservation recommendations from this study include the urgent need to establish population monitoring studies in the Critically Endangered Kemp’s ridley and hawksbill and the Data Deficient flatback. Furthermore, population monitoring programs should be implemented in Southeast Asia and Northern and Central Africa, where knowledge on sea turtle populations is still limited. Finally, due to the long-distance movements of sea turtles, we also advocate for international cooperation and collaboration of local communities to protect these ecologically important and iconic marine species. Full article

The increasing amount of space debris poses a significant threat to operational satellites and space-based services. This article updates the community on the current status of the development of ATLAS, a tracking radar that is part of the EUSST network and aims to detect space objects in low Earth orbits. This article focuses on the latest activities performed: calibration of the pointing system and initial observations of space objects. The calibration procedure consisted of cross-scanning the Solar disk and yielded great results, obtaining an offset of 5.3° in azimuth and 0.10° in elevation. The first observation campaign resulted in 33 range detections of the International Space Station (ISS) with a probability of false alarm of ${10}^{-9}$. The observations were then used to readjust the radar equation to assess the real-world performance of the system. Full article

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts. Full article

The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates, usually called ‘the clones of high risk’, often drive the spread of resistance within particular species. Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole-genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and the development of spread prevention measures. However, the availability and uniformity of the data derived from genomic sequences often represent a bottleneck for such investigations. With this dataset, we present the results of a genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen, Acinetobacter baumannii. Important typing information, including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic bla_OXA-51-like gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. These data will be useful for researchers in the field of A. baumannii genomic epidemiology, resistance analysis, and prevention measure development. Full article

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure. Full article

Environmental pollution is a growing threat to natural ecosystems and one of the world’s most pressing concerns. The increasing worldwide use of pharmaceuticals has elevated their status as significant emerging contaminants. Pharmaceuticals enter aquatic environments through multiple pathways related to anthropogenic activity. Their high consumption, insufficient waste treatment, and the incapacity of organisms to completely metabolize them contribute to their accumulation in aquatic environments, posing a threat to all life forms. Various analytical methods have been used to quantify pharmaceuticals. Biotechnology advancements based on next-generation sequencing (NGS) techniques, like eDNA metabarcoding, have enabled the development of new methods for assessing and monitoring the ecotoxicological effects of pharmaceuticals. eDNA metabarcoding is a valuable biomonitoring tool for pharmaceutical pollution because it (a) provides an efficient method to assess and predict pollution status, (b) identifies pollution sources, (c) tracks changes in pharmaceutical pollution levels over time, (d) assesses the ecological impact of pharmaceutical pollution, (e) helps prioritize cleanup and mitigation efforts, and (f) offers insights into the diversity and composition of microbial and other bioindicator communities. This review highlights the issue of aquatic pharmaceutical pollution while emphasizing the importance of using modern NGS-based biomonitoring actions to assess its environmental effects more consistently and effectively. Full article

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study’s results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape. Full article

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent-based simulator will enable policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM), which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of the PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of COVID-19 are used to validate the proposed model. However, they are highly extendable to all pandemics in general, enabling robust planning for more sustainable communities. Full article

The unprecedented COVID-19 pandemic posed major challenges to local, regional, and global economies and health systems, and fast clinical diagnostic workflows were urgently needed to contain the spread of SARS-CoV-2. Here, we describe the platform and workflow established at the Cornell COVID-19 Testing Laboratory (CCTL) for the high-throughput testing of clinical samples from the university and the surrounding community. This workflow enabled efficient and rapid detection and the successful control of SARS-CoV-2 infection on campus and its surrounding communities. Our cost-effective and fully automated workflow enabled the testing of over 8000 pooled samples per day and provided results for over 2 million samples. The automation of time- and effort-intensive sample processing steps such as accessioning and pooling increased laboratory efficiency. Customized software applications were developed to track and store samples, deconvolute positive pools, track and report results, and for workflow integration from sample receipt to result reporting. Additionally, quality control dashboards and turnaround-time tracking applications were built to monitor assay and laboratory performance. As infectious disease outbreaks pose a constant threat to both human and animal health, the highly effective workflow implemented at CCTL could be modeled to establish regional high-capacity testing hubs for infectious disease preparedness and emergency response. Full article