12,315 research outputs found
Revisão taxonómica do género Calendula L. (Asteraceae - Calenduleae) na Península Ibérica e Marrocos
The genus Calendula L. (Asteraceae - Calenduleae) includes, depending on the author, 10 to 25 species, distributed mainly in the Mediterranean basin. The taxonomy of this genus is considered to be extremely difficult, due to a great morphological variability, doubtfull relevance of some of the characters used to distinguish its species (e.g. the life form: annual or perennial; the habit: erect or diffuse, shape of the leaves, indumentum, relative size of the capitula and colour of disc or ray florets, achene morphology), but also due to the hybridization and polyploidization. Despite the numerous studies that have been published, no agreement on the classification and characters used to discriminate between taxa has been reached. A taxonomic study of the genus Calendula was conducted for the Iberian Peninsula and Morocco, aiming at (1) access the morphological variability between and within taxa, (2) confirm the chromosome numbers, (3) increase the nuclear DNA content estimations, (4) re-evaluate taxa delimitations and circumscription, and (5) reassess, and redefine, the
descriptions and characters useful to distinguish taxa. In order to achieve a satisfying taxonomic core, extensive fieldwork, detailed morphometric analysis,
chorological, karyological and genome size studies were conducted. For the Iberian Peninsula, four species were recognized, including nine subspecies (between these two new subspecies were described). For Morocco, including some taxa from Algeria and Tunisia 13 species were recognized (two new species and a nomenclatural change), including 15 subspecies (among these eight new subspecies were described). To corroborate the results obtained and to evaluate the evolutionary relationships among taxa, phylogenetic studies using molecular methods, such as ITS, microsatellites or other molecular markers, should be used.O género Calendula L. (Asteraceae - Calenduleae) inclui, dependendo do autor, 10 a 25 espécies, distribuídas essencialmente na bacia do Mediterrâneo. A taxonomia deste género é considerada extremamente difícil, devido à grande variabilidade morfológica, discutivel relevância de alguns dos caracteres utilizados para distinguir suas espécies (por exemplo, a forma de vida: anual ou perene, o hábito: erecto ou difuso, a forma das folhas, o indumento, o tamanho e a cor dos capítulos e a morfologia dos aquénios), mas também devido à
hibridização e poliploidização. Apesar dos inúmeros estudos que foram publicados, não foi alcançado um acordo sobre a classificação e os caracteres utilizados para discriminar as suas espécies. Um estudo taxonómico do género Calendula foi realizado para a Península Ibérica e Marrocos, com o objectivo de (1) verificar a variabilidade morfológica, (2) confirmar o número de cromossomas, (3) aumentar as estimativas de conteúdo em ADN, (4) reavaliar a delimitação e a circunscrição dos taxa, e (5) reavaliar e redefinir as descrições e caracteres úteis para os distinguir. Para alcançar uma robustês taxonómica satisfatória, foram realizados extensos trabalhos de campo, análise morfométrica detalhada, abordagens corológicas, cariológicas e quanto ao conteúdo em ADN. Para a Península Ibérica, quatro espécies foram reconhecidas, incluindo nove subespécies (entre essas duas novas subespécies foram descritas). Para Marrocos, incluindo alguns taxa da Argelia e Tunisia, foram reconhecidas 13 espécies (duas novas e uma mudança nomenclatural), incluindo 15 subespécies (entre essas oito novas subespécies foram descritas). Para corroborar os resultados obtidos e avaliar as relações evolutivas e filogenéticas entre os taxa, estudos que utilizem diferentes métodos
moleculares, tais como ITS, microsatélites ou outros marcadores moleculares, devem ser utilizados.Apoio financeiro do Laboratório Associado CESAM - Centro de Estudos do Ambiente e do Mar (AMB/50017) financiado por fundos nacionais através da FCT/MCTES e cofinanciado pelo FEDER (POCI-01-0145-FEDER-007638), no âmbito do Acordo de Parceria PT2020, e Compete 2020Programa Doutoral em Biologi
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Re-prioritizing climate services for agriculture: Insights from Bangladesh
Considerable progress has been made in establishing climate service capabilities over the last few decades, but the gap between the resulting services and national needs remains large. Using climate services for agriculture in Bangladesh as a case study example, we highlight mismatches between local needs on the one hand, and international initiatives that have focused largely on prediction on the other, and we make suggestions for addressing such mismatches in similar settings. To achieve greater benefit at the national level, there should be a stronger focus on addressing important preliminaries for building services. These preliminaries include the identification of priorities, the definition of responsibilities and expectations, the development of climate services skills, and the construction of a high-quality and easily usable national climate record. Once appropriate institutional, human resources and data infrastructure are in place, the implementation of a climate monitoring and watch system would form a more logical basis for initial climate service implementation than attempting to promote sub-seasonal to seasonal climate forecasting, especially when and where the inherent predictability is limited at best. When and where forecasting at these scales is viable, efforts should focus on defining and predicting high-impact events important for decision making, rather than on simple seasonal aggregates that often correlate poorly with outcomes. Some such forecasts may be more skillful than the 3- to 4-month seasonal aggregates that have become the internationally adopted standard. By establishing a firm foundation for climate services within National Meteorological Services, there is a greater chance that individual climate service development initiatives will be sustainable after their respective project lifetimes
What do new performance metrics, VeDBA and Dynamic yaw, tell us about energy-intensive activities in whale sharks?
During oscillatory dives, whale sharks (Rhincodon typus) expend varying levels of energy in active ascent and passive descent. They are expected to minimise movement costs by travelling at optimum speed unless having reason to move faster, for example during feeding or evasion of danger. A proxy for power, dynamic body acceleration (DBA) has previously been used to identify whale shark movement patterns but has yet been used to identify occasions where power is elevated above minimum requirements. 59 hours of biologging data from 13 juvenile whale sharks (Ningaloo Reef, Western Australia) including depth, body pitch angle, magnetometry and DBA, was analysed to investigate minimum power requirements for dives and identify events of elevated power. Dynamic yaw (the rate of change of heading), a new proxy for power, was introduced to determine its effectiveness compared to the already-established DBA. The relationship between pitch angle and these two proxies was investigated to determine which had the stronger relationship. Dynamic yaw produced a poor relationship with pitch angle compared to DBA, and thus DBA was selected as the focus proxy for the remainder of the study. DBA was utilised to produce a minimum power trend versus body pitch angle using a convex hull analysis which allowed for the identification of proxy for power utilisation above the minimum (PAM). 16 instances of PAM were identified in 59 hours of data, which could all be considered instances where energy minimisation is not prioritised, such as feeding or avoidance. The PAM method was capable of identifying instances where energy minimisation is not prioritised, and therefore has future implications in investigations of location-specific behaviours in relation to feeding and anthropogenic disturbance
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació
Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment
Internet of Things (IoT) is an emerging technology and its applications are flattering amidst many users, as it makes everything easier. As a consequence of its massive growth, security and privacy are becoming crucial issues where the IoT devices are perpetually vulnerable to cyber-attacks. To overcome this issue, intrusion detection and mitigation is accomplished which enhances the security in IoT networks. In this paper, we proposed Blockchain entrenched Bi-level intrusion detection and graph based mitigation framework named as HybridChain-IDS. The proposed work embrace four sequential processes includes time-based authentication, user scheduling and access control, bi-level intrusion detection and attack graph generation. Initially, we perform time-based authentication to authenticate the legitimate users using NIK-512 hashing algorithm, password and registered time are stored in Hybridchain which is an assimilation of blockchain and Trusted Execution Environment (TEE) which enhances data privacy and security. After that, we perform user scheduling using Cheetah Optimization Algorithm (COA) which reduces the complexity and then the access control is provided to authorized users by smart contract by considering their trust and permission level. Then, we accomplish bi-level intrusion detection using ResCapsNet which extracts sufficient features and classified effectively. Finally, risk of the attack is evaluated, and then the attacks graphs are generated by employing Enhanced k-nearest neighbor (KNN) algorithm to identify the attack path. Furthermore, the countermeasures are taken based on the attack risk level and the attack graph is stored in Hybridchain for eventual attack prediction. The implementation of this proposed work is directed by network simulator of NS-3.26 and the performance of the proposed HybridChain-IDS is enumerated based on various performance metrics
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks
Botnet attacks are a major threat to networked systems because of their
ability to turn the network nodes that they compromise into additional
attackers, leading to the spread of high volume attacks over long periods. The
detection of such Botnets is complicated by the fact that multiple network IP
addresses will be simultaneously compromised, so that Collective Classification
of compromised nodes, in addition to the already available traditional methods
that focus on individual nodes, can be useful. Thus this work introduces a
collective Botnet attack classification technique that operates on traffic from
an n-node IP network with a novel Associated Random Neural Network (ARNN) that
identifies the nodes which are compromised. The ARNN is a recurrent
architecture that incorporates two mutually associated, interconnected and
architecturally identical n-neuron random neural networks, that act
simultneously as mutual critics to reach the decision regarding which of n
nodes have been compromised. A novel gradient learning descent algorithm is
presented for the ARNN, and is shown to operate effectively both with
conventional off-line training from prior data, and with on-line incremental
training without prior off-line learning. Real data from a 107 node packet
network is used with over 700,000 packets to evaluate the ARNN, showing that it
provides accurate predictions. Comparisons with other well-known state of the
art methods using the same learning and testing datasets, show that the ARNN
offers significantly better performance
Submarine groundwater discharge in Dongshan Bay, China: A master regulator of nutrients in spring and potential national significance of small bays
Despite over 90% of China’s coastal bays have an area less than 500 km2, the geochemical effects of SGD on those ecosystems are ambiguous. Based on mapping and time-series observations of Ra isotopes and nutrients, a case study of small bays (<500 km2), we revealed that submarine groundwater discharge (SGD) predominately regulated the distribution of nutrients and fueled algal growth in Dongshan Bay, China. On the bay-wide scale, the SGD rate was estimated to be 0.048 ± 0.022 m day−1 and contributed over 95% of the nutrients. At the time-series site where the bay-wide highest Ra activities in the bottom water marked an SGD hotspot with an average rate an order of magnitude greater, the maximum chlorophyll concentration co-occurred, suggesting that SGD may support the algal bloom. The ever-most significant positive correlations between 228Ra and nutrients throughout the water column (P< 0.01, R2 > 0.90 except for soluble reactive phosphorus in the surface) suggested the predominance of SGD in controlling nutrient distribution in the bay. Extrapolated to a national scale, the SGD-carried dissolved inorganic nitrogen flux in small bays was twice as much as those in large bays (>2,000 km2). Thus, the SGD-carried nutrients in small bays merit immediate attention in environmental monitoring and management
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
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