Search

This study aimed to investigate the nonlinear structural behavior of concrete deep beams internally reinforced with glass fiber-reinforced polymer (GFRP) reinforcing bars and containing a web opening of various sizes and locations within the shear span. Three-dimensional (3D) numerical simulation models were developed for large-scale GFRP-reinforced concrete deep beams (300 mm × 1200 mm × 5000 mm) with a shear span-to-depth ratio (a/h) of 1.04. Predictions of the numerical models were validated against published experimental data. A parametric study was conducted to examine the effect of varying the opening size and location on the shear response. Results of the numerical analysis indicated that the strength of the deep beam models with an opening in the middle of the shear span decreased with an increase in either the opening width or height. The rate of the strength reduction caused by increasing the opening height was, however, more significant than that produced by increasing the opening width. Placing a web opening in the compression zone close to the load plate was very detrimental to the beam strength. Conversely, a negligible strength reduction was recorded when the web opening was placed in the tension side above the flexural reinforcement and away from the natural load path. Data of the parametric study were utilized to introduce simplified analytical formulas capable of predicting the shear capacity of GFRP-reinforced concrete deep beams with a web opening in the shear span. [ABSTRACT FROM AUTHOR]

The Dark Web is a subset of the Deep Web, requiring special browsers, the Dark Net refers to encrypted networks, the Deep Web encompasses non-indexed online content, and darknet includes unused IP address networks. The Dark Net has become a hotbed of cybercrime, with individuals and groups using the anonymity and encryption provided by the network to carry out a range of criminal activities. One of the most concerning trends in recent years has been the weaponization of cybercrimes, as criminals use their technical skills to create tools and techniques that can be used to launch attacks against individuals, businesses, and governments. This paper examines the weaponization of cybercrimes on the Dark Net, focusing on the question of detection and application. This paper uses a Systematic Literature Review (SLR) method to appraise the Dark Web, examine the crimes and their consequences and identify future measures to reduce crime threats. Data from 88 relevant articles from 2011 to 2023 were extracted and synthesized, along with the latest data from 2024 to answer research questions, providing comprehensive knowledge on growing crimes; assessing social, economic, and ethical impacts; and analyzing established techniques and methods to locate and apprehend criminals. [ABSTRACT FROM AUTHOR]

Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its goal is to evaluate their contributions and impact in the academic community, as well as to determine their reputation and status within specific disciplinary domains. Web of Science (WOS), being the most renowned global academic citation database, provides crucial data for academic evaluation. However, due to factors such as institutional changes, translation discrepancies, transcription errors in databases, and authors' individual writing habits, there exist ambiguities in the institution names recorded in the WOS literature, which in turn affect the scientific evaluation of researchers and institutions. To address the issue of data reliability in academic evaluation, this paper proposes a WOS institution name synonym recognition framework that integrates multi-granular embeddings and multi-contextual information. [ABSTRACT FROM AUTHOR]

The choice of a good join order plays an important role in the query performance of databases. However, determining the best join order is known to be an NP-hard problem with exponential growth with the number of joins. Because of this, nonlearning approaches to join order optimization have a longer optimization and execution time. In comparison, the models of machine learning, once trained, can construct optimized query plans very quickly. Several efforts have applied machine learning to optimize join order for SQL queries outperforming traditional approaches. In this work, we suggest a reinforcement learning technique for join optimization for SPARQL queries, ReJOOSp. SPARQL queries typically contain a much higher number of joins than SQL queries and so are more difficult to optimize. To evaluate ReJOOSp, we further develop a join order optimizer based on ReJOOSp and integrate it into the Semantic Web DBMS Luposdate3000. The evaluation of ReJOOSp shows its capability to significantly enhance query performance by achieving high-quality execution plans for a substantial portion of queries across synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

The use of the unindexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is a dangerous place where all kinds of criminal activities take place, Despite advances in web forensic techniques, tools, and methodologies, few studies have formally tackled dark and deep web forensics and the technical differences in terms of investigative techniques and artefact identification and extraction. This study proposes a novel and comprehensive protocol to guide and assist digital forensic professionals in investigating crimes committed on or via the deep and dark web. The protocol, named D2WFP, establishes a new sequential approach for performing investigative activities by observing the order of volatility and implementing a systemic approach covering all browsing-related hives and artefacts which ultimately resulted in improving the accuracy and effectiveness. Rigorous quantitative and qualitative research has been conducted by assessing the D2WFP following a scientifically sound and comprehensive process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting the D2WFP which outperforms any current industry or opensource browsing forensic tools. The second contribution of the D2WFP is the robust formulation of artefact correlation and cross-validation within the D2WFP which enables digital forensic professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts. [ABSTRACT FROM AUTHOR]

The untraceable part of the Deep Web, also known as the Dark Web, is one of the most used "secretive spaces" to execute all sorts of illegal and criminal activities by terrorists, cybercriminals, spies, and offenders. Identifying actions, products, and offenders on the Dark Web is challenging due to its size, intractability, and anonymity. Therefore, it is crucial to intelligently enforce tools and techniques capable of identifying the activities of the Dark Web to assist law enforcement agencies as a support system. Therefore, this study proposes four deep learning architectures (RNN, CNN, LSTM, and Transformer)-based classification models using the pre-trained word embedding representations to identify illicit activities related to cybercrimes on Dark Web forums. We used the Agora dataset derived from the DarkNet market archive, which lists 109 activities by category. The listings in the dataset are vaguely described, and several data points are untagged, which rules out the automatic labeling of category items as target classes. Hence, to overcome this constraint, we applied a meticulously designed human annotation scheme to annotate the data, taking into account all the attributes to infer the context. In this research, we conducted comprehensive evaluations to assess the performance of our proposed approach. Our proposed BERT-based classification model achieved an accuracy score of 96%. Given the unbalancedness of the experimental data, our results indicate the advantage of our tailored data preprocessing strategies and validate our annotation scheme. Thus, in real-world scenarios, our work can be used to analyze Dark Web forums and identify cybercrimes by law enforcement agencies and can pave the path to develop sophisticated systems as per the requirements. [ABSTRACT FROM AUTHOR]

Waste Glass Powder (WGP) could be used as a cement replacement additive to manufacture concrete and solving the problem of environmental pollution. The experimental program was made up of ten simply supported reinforced High-Strength Concrete (HSC) deep beams tested under static loadings. Five beams were with WGP, while the other five beams were without WGP. Eight beams had web openings while two reference beams were without openings. The principal studied parameters were the effect of using WGP, and the location and size of web openings. Using the three-dimensional finite element computer program ABAQUS, a numerical simulation for comparing the shear strength and behavior of tested deep beams has been suggested. The comparison between experimental failure loads of studied beams with that estimated by the Strut-and-Tie model was carried out. Three codes of practice were used to make this comparison: the American Concrete Institute (ACI 318-19), the New Zealand Code (NZS-06), and the Japan Society of Civil Engineering (JSCE-07). The results showed that using WGP in similar deep beams with web openings enhances the cracking shear strength (by about 17–25%) and the ultimate shear strength (by about 12–41%). The improvement in the ultimate failure load could be attributed to the developed concrete microstructures caused by WGP's very fine grains, producing further gel, and decreasing the number of voids in the concrete matrix. The suggested finite element simulation accurately predicts the behavior of HSC deep beams with and without WGP beams and with web openings. [ABSTRACT FROM AUTHOR]

The World Wide Web (www. consists of the surface web, deep web, and Dark Web, depending on the content shared and the access to these network layers. Dark Web consists of the Dark Net overlay of networks that can be accessed through specific software and authorization schema. Dark Net has become a growing community where users focus on keeping their identities, personal information, and locations secret due to the diverse population base and well-known cyber threats. Furthermore, not much is known of Dark Net from the user perspective, where often there is a misunderstanding of the usage strategies. To understand this further, we conducted a systematic analysis of research relating to Dark Net privacy and security on N = 200 academic papers, where we also explored the user side. An evaluation of secure end-user experience on the Dark Net establishes the motives of account initialization in overlaid networks such as Tor. This work delves into the evolution of Dark Net intelligence for improved cybercrime strategies across jurisdictions. The evaluation of the developing network infrastructure of the Dark Net raises meaningful questions on how to resolve the issue of increasing criminal activity on the Dark Web. We further examine the security features afforded to users, motives, and anonymity revocation. We also evaluate more closely nine user-study-focused papers revealing the importance of conducting more research in this area. Our detailed systematic review of Dark Net security clearly shows the apparent research gaps, especially in the user-focused studies emphasized in the paper. [ABSTRACT FROM AUTHOR]

The Internet has played a major role in the distribution of New Psychoactive Substances (NPS), and crypto markets are increasingly used for the anonymous sale of drugs, including NPS. This study explores the availability of individual NPS and vendors on the crypto markets and considers whether crypto markets are a reliable platform for the sale of NPS. Data was collected from 22 crypto markets that were accessed through the hidden web using the Onion Router (Tor). Data collection took place bimonthly from October 2015 to October 2016 as part of the CASSANDRA (Computer Assisted Solutions for Studying the Availability aNd DistRibution of novel psychoActive substances) project. In seven snapshots over 12 months, 808 unique vendors were found selling 256 unique NPS. The total number of individual NPS and vendors increased across the data collection period (increase of 93.6% and 71.6%, respectively). Only 24% (n = 61) of the total number of NPS and 4% (n = 31) of vendors appeared in every snapshot over the 12 months, whereas 21% (n = 54) of NPS and 45% (n = 365) of vendors only appeared once throughout the data collection. The individual NPS and vendors did not remain the same over the 12 months. However, the availability of NPS and vendors selling NPS grew. NPS consistently available on crypto markets could indicate popular substances. [ABSTRACT FROM AUTHOR]

Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China's 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User's accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection. [ABSTRACT FROM AUTHOR]

The quantification of corneal endothelial cell (CEC) morphology using manual and semi-automatic software enables an objective assessment of corneal endothelial pathology. However, the procedure is tedious, subjective, and not widely applied in clinical practice. We have developed the CellsDeepNet system to automatically segment and analyse the CEC morphology. The CellsDeepNet system uses Contrast-Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast of the CEC images and reduce the effects of non-uniform image illumination, 2D Double-Density Dual-Tree Complex Wavelet Transform (2DDD-TCWT) to reduce noise, Butterworth Bandpass filter to enhance the CEC edges, and moving average filter to adjust for brightness level. An improved version of U-Net was used to detect the boundaries of the CECs, regardless of the CEC size. CEC morphology was measured as mean cell density (MCD, cell/mm2), mean cell area (MCA, μm2), mean cell perimeter (MCP, μm), polymegathism (coefficient of CEC size variation), and pleomorphism (percentage of hexagonality coefficient). The CellsDeepNet system correlated highly significantly with the manual estimations for MCD (r = 0.94), MCA (r = 0.99), MCP (r = 0.99), polymegathism (r = 0.92), and pleomorphism (r = 0.86), with p < 0.0001 for all the extracted clinical features. The Bland–Altman plots showed excellent agreement. The percentage difference between the manual and automated estimations was superior for the CellsDeepNet system compared to the CEAS system and other state-of-the-art CEC segmentation systems on three large and challenging corneal endothelium image datasets captured using two different ophthalmic devices. [ABSTRACT FROM AUTHOR]

This study investigates the possibility of replacing the minimum web reinforcement in deep beams with discrete fibers. Additionally, the equivalent dosage of fibers required to obtain similar performance of the deep beam with minimum web reinforcement is investigated. Deep beams made of plain concrete with no fibers, beams with minimum web reinforcement as per AASHTO LFRD recommendations (0.3% in both horizontal and vertical), and with a 0.5% volume fraction of steel, macro-synthetic and hybrid fibers are tested at a shear span to height ratio (a/h) of one. Test results show that the presence of 0.3% web reinforcement in horizontal and vertical directions increased the peak load by 25% compared to the plain concrete beams. However, it did not significantly change the first diagonal crack load. With the addition of 0.5% of steel, macro-synthetic and hybrid fibers, the peak load increased by 49%, 42%, and 63%, respectively, compared to the plain concrete specimen. The addition of steel fibers significantly improved the first cracking load. In contrast, macro-synthetic fibers did not affect the first cracking load but improved the ductility with higher deflections at peak. Hybridization of steel and macro synthetic fibers showed improved performance compared to the individual fibers of the same volume in peak load and ductility. Test results showed that a 0.5% volume fraction of discrete macro steel or synthetic or hybrid fibers can be used to completely replace the minimum web reinforcement (0.3% in both directions). [ABSTRACT FROM AUTHOR]

With the growth of artificial intelligence in healthcare and biomedical research, many researchers are interested in large amounts of data in hospitals and medical research centers. Then the need for remote medicine services and clinical data utilization are expanding. However, since the misuse and abuse of clinical data causes serious problems, the scope of its use is bound to have a limited range physically and logically. Then a security-enhanced data distribution system for medical deep web environments. Therefore, in this paper, we propose a blockchain-based clinical data management model named DeepBlockshield to prevent information leakage between the deep web and the surface web. Blockchain supports data integrity and user validation to support data sharing in closed networks. Meanwhile, the agent performs integrity verification between the blockchain and the deep web and strengthens the security between the surface web and the deep web. DeepBlockShield verifies the user's validity through the records of the deep web and blockchain. Furthermore, we wrap the results analyzed by the valid request into a web interface and provide information to the requester asynchronously. In the experiment, the block generation cycle and size on the delay time was analyzed for verifying the stability of the blockchain network. As a result, it showed that the proposed approach guarantees the integrity and availability of clinical data in the deep web environment. [ABSTRACT FROM AUTHOR]

What are the New/Novel Psychoactive Substances (NPS): Definitions New/novel psychoactive substances (NPS) are defined as new narcotic/psychotropic drugs which are not controlled by the United Nations' 1961 Narcotic Drugs/1971 Psychotropic Substances conventions, but which may pose a public health threat [[1]]. Eventually, the NPS would move into "head shops" and/or the "street" market, then reported by formal early warning systems, and new legislation would be implemented to counter the index NPS/substance. As commented in [[9]], although the Act may have achieved a reduction in UK-based online stores and offline retail stores, the easily tracked open-web drug transactions could be displaced by alternative routes such as street-level drug dealing or crypto markets. [Extracted from the article]

Recent advances in semantic web and deep learning technologies enable new means for the computational analysis of vast amounts of information from the field of digital humanities. We discuss how some of the techniques can be used to identify historical and cultural symmetries between different characters, locations, events or venues, and how these can be harnessed to develop new strategies to promote intercultural and cross-border aspects that support the teaching and learning of history and heritage. The strategies have been put to the test in the context of the European project CrossCult, revealing enormous potential to encourage curiosity to discover new information and increase retention of learned information. [ABSTRACT FROM AUTHOR]

The success of the Silk Road has prompted the growth of many Dark Web marketplaces. This exponential growth has provided criminal enterprises with new outlets to sell illicit items. Thus, the Dark Web has generated great interest from academics and governments who have sought to unveil the identities of participants in these highly lucrative, yet illegal, marketplaces. Traditional Web scraping methodologies and investigative techniques have proven to be inept at unmasking these marketplace participants. This research provides an analytical framework for automating Dark Web scraping and analysis with free tools found on the World Wide Web. Using a case study marketplace, we successfully tested a Web crawler, developed using AppleScript, to retrieve the account information for thousands of vendors and their respective marketplace listings. This paper clearly details why AppleScript was the most viable and efficient method for scraping Dark Web marketplaces. The results from our case study validate the efficacy of our proposed analytical framework, which has relevance for academics studying this growing phenomenon and for investigators examining criminal activity on the Dark Web. [ABSTRACT FROM AUTHOR]

Since the rise of the Islamic State of Iraq and Syria (ISIS, also known as Daesh and ISIL) in 2014, antiquities have been a widely publicized source of funding for what has become one of the most technologically savvy terrorist organizations of the modern era. The globalization of technology and rise of popularity in cryptocurrencies has changed the face of black-market trade and the actors that carry out these crimes. While art and antiquities have long served as a market with susceptibilities to laundering, the emergence of Dark Web markets, identification-masking software, and untraceable cryptocurrencies such as Bitcoin have opened new doors to potential vulnerabilities. The anonymity that is offered by these technologies acts as a roadblock for authorities, while attracting the likes of terrorists and transnational criminals. Investigative research using cyber security platforms to identify digital artifacts connected to potential traffickers provides the opportunity to unmask the seemingly untraceable actors behind these activities. The evidence of illicit antiquities trafficking on the Dark Web displayed in this article can generate a new discussion on how and where to study black-market antiquities to gain needed insight into combating the illicit trade online and the transnational criminal groups it may finance. [ABSTRACT FROM AUTHOR]