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In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments. [ABSTRACT FROM AUTHOR]

Content on the World Wide Web that is not indexable by standard search engines defines a category called the deep Web. Dark networks are a subset of the deep Web. They provide services of great interest to users who seek online anonymity during their search on the Internet. Tor is the most widely used dark network around the world. It requires unique application layer protocols and authorization schemes to access. The present evidence reveals that in spite of great efforts to investigate Tor, our understanding is limited to the work on either the information or structure of this network. Also, interplay between information and structure that plays an important role in evaluating socio-technical systems including Tor has not been given the attention it deserves. In this article, we review and classify the present work on Tor to improve our understanding of this network and shed light on the new directions to evaluate Tor. The related work can be categorized into proposals that (1) study the security and privacy on Tor, (2) characterize Tor's structure, (3) evaluate the information hosted on Tor, and (4) review the related work on Tor from 2014 to the present. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018. Design/methodology/approach: We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure. Findings: Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input. Practical implications: These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation. Originality/value: Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018. [ABSTRACT FROM AUTHOR]

Introduction: Plastics carried in the outflow of major rivers can be made available and subsequently ingested by marine fishes, causing lethal and sublethal effects. Highly abundant, vertically migrating myctophids play a crucial role in facilitating nutrient cycling between the epi- and mesopelagic zones. However, this diel movement may also make myctophids significant conduits for transporting ingested microparticles from surface waters to deeper food webs. Methods: We examined the gastrointestinal tracts of 340 myctophids caught at varying distances from the Columbia River mouth in the epipelagic zone of the northeast Pacific Ocean to determine if proximity to a presumed point source influences microparticle ingestion. Results: While we found no direct spatial connection with ingestion frequency, we discovered that (a) ~34% of myctophids had either synthetic or other anthropogenic particles retained in their GI tract, (b) microparticle ingestion was higher in an active-feeding species of myctophid (Tarletonbeania crenularis) than an inactive-feeding species (Stenobrachius leucopsarus), and (c) species and standard length were the most influential predictors of microparticle consumption in our best fit model. Discussion: Our failure to detect a significant relationship between distance from a source and ingestion by myctophids is likely due to the particles undergoing fluctuations in dispersal patterns once they enter the ocean, particularly for microfibers which can be transported across large distances. Biological factors like body size may be more relevant to understanding microparticle ingestion patterns in mesopelagic fishes. Overall, our study highlights the potential role myctophids serve as multidirectional transporters of microparticles in Northern California Current food webs, with potential impacts on fisheries and human food systems. [ABSTRACT FROM AUTHOR]

Among adolescents, HIV/AIDs remains a significant cause of death globally [1–4]. Given the unique stages in human development, adolescents have been shown to fall within a sexually active phase. Combined with other social and structural factors in their immediate environments, HIV prevention and care among adolescents can be filled with challenges for intervention. This paper outlines this protocol to systematically review peer-reviewed literature in prevention and care among adolescents 10–19 years. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be used to report this scoping review. The review will involve screening and extracting data using covidence as the primary tool. The review will encompass quantitative, qualitative, and mixed methods studies, utilizing a search strategy from electronic databases such as PubMed (NCBI), Web of Science Core Collection (Clarivate), Embase (Elsevier), and Scopus (Elsevier). Additionally, a search will be conducted for grey literature using Global Index Medicus (WHO), MedNar (Deep Web Technologies), and Central Register of Controlled Trials (Cochrane). Duplicate removal and selection of articles that meet the inclusion criteria for the study will be performed using Covidence. Once the screening process is complete, data will be extracted from the full-text screened articles in Covidence. We will pilot the extracted data in Covidence to ensure that all relevant information has been captured, making necessary changes if required. Data extraction will be carried out by at least two authors, with any conflicts resolved by the same authors. If a conflict cannot be resolved between the two, a third author will make a final determination. We aim to analyze data thematically by employing a grounded theory approach to generate codes pertinent to the research question. The team will review and discuss codes to create a cohesive set of codes that will be instrumental in identifying knowledge gaps and constructing themes that summarize the data. The proposed systematic review will be among the pioneering efforts to rigorously assess global data on HIV prevention and care, with a specific focus on adolescents 10–19 years. It will consider the diverse socio-economic factors and experiences shaping these adolescents' lives in HIV prevention and care. We expect this review to yield critical insights into the present landscape of HIV prevention and care for individuals aged 10–19. These findings will also play a pivotal role in shaping the development of a global framework that researchers and stakeholders can readily adopt and implement across socio-economic contexts. This framework will aim to address the unique needs of all adolescents concerning HIV prevention and care. [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]

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 symbiotic relationship between the human digestive system and its intricate microbiota is a captivating field of study that continues to unfold. Comprising predominantly anaerobic bacteria, this complex microbial ecosystem, teeming with trillions of organisms, plays a crucial role in various physiological processes. Beyond its primary function in breaking down indigestible dietary components, this microbial community significantly influences immune system modulation, central nervous system function, and disease prevention. Despite the strides made in microbiome research, the precise mechanisms underlying how bacterial effector functions impact mammalian and microbiome physiology remain elusive. Unlike the traditional DNA-RNA-protein paradigm, bacteria often communicate through small molecules, underscoring the imperative to identify compounds produced by human-associated bacteria. The gut microbiome emerges as a linchpin in the transformation of natural products, generating metabolites with distinct physiological functions. Unraveling these microbial transformations holds the key to understanding the pharmacological activities and metabolic mechanisms of natural products. Notably, the potential to leverage gut microorganisms for large-scale synthesis of bioactive compounds remains an underexplored frontier with promising implications. This review serves as a synthesis of current knowledge, shedding light on the dynamic interplay between natural products, bacteria, and human health. In doing so, it contributes to our evolving comprehension of microbiome dynamics, opening avenues for innovative applications in medicine and therapeutics. As we delve deeper into this intricate web of interactions, the prospect of harnessing the power of the gut microbiome for transformative medical interventions becomes increasingly tantalizing. [ABSTRACT FROM AUTHOR]

Background Objectives Search Strategy Selection Criteria Data Collection and Analysis Main Results Conclusions Clinical and preclinical evidence indicate that in utero maternal asthma exposure increases progeny asthma risk. Whether maternal asthma also increases the risks of progeny allergy is unclear.To synthesise the available evidence on the relationship between in utero exposure to maternal asthma and postnatal asthma, wheezing and allergic diseases (Prospero: CRD42020201538).We systematically searched MEDLINE [PubMed], Embase [Ovid], Web of Science, Informit Health, the Cochrane Library, CINAHL [EBSCOhost], MedNar [Deep Web Technologies], ProQuest Theses and Dissertations, Scopus [Elsevier] and Trove, to the end of 2023.Studies reporting asthma, wheeze and/or allergic disease in progeny of women with and without asthma or with asthma classified by control, exacerbation or severity.Double screening, selection, data extraction and quality assessment were performed, using Joanna Briggs Institute (JBI) scoring.Of 134 non‐overlapping studies, 127 were included in ≥1 meta‐analysis. Maternal asthma ever was associated with greater risks of asthma (65 studies, risk ratio [95% confidence interval] 1.76 [1.57–1.96]), wheeze (35 studies, 1.59 [1.52–1.66]), food allergy (5 studies, 1.32 [1.23–1.40]), allergic rhinitis (7 studies, 1.18 [1.06–1.31]) and allergic dermatitis (14 studies, 1.17 [1.11–1.23]) ever in progeny. Asthma during the pregnancy, more severe, and uncontrolled maternal asthma were each associated with greater risks of progeny asthma.Children of mothers with asthma are at increased risk for the development of allergic diseases. Whether improved maternal asthma control reduces risks of child allergy as well as asthma requires further investigation. [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]

How to efficiently find what you want from massive data has become a hot topic. It is precisely because network information is developing so fast that so many data-oriented software systems and pages are used to manage so much network information. Aiming at problems such as insufficient information mining of visual communication websites in ASPNET and poor visual communication effects, a design scheme for integrating high-dimensional data into visual communication websites is proposed. Its working architecture includes high-dimensional data integration, multi-type visual integration, and visual support services. The main functions of the website include comprehensive management, charts, data sources, projects, systems, etc. We have conducted specific design and Implementation of high-dimensional information integration on the website, completed the design and Implementation of integration of high-dimensional data, integration of multiple visualization types, and integrated access to visualization-supported functions. Use a deep web crawler to search the visual site. It uses a search-based approach to filter URLs and crawl valuable web pages. Practice has proved that the intelligent distribution network management page implemented by the solution proposed in this article has good performance. The system shows good performance in visualization implementation and the ability to discover information. The system achieved sound and visual communication effects. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases lead to a gradual decline in cognitive and motor functions due to the progressive loss of neurons in the central nervous system. The role of dual leucine zipper kinase (DLK) in regulating stress responses and neuronal death pathways highlights its significance as a target against neurodegenerative diseases. The non-availability of FDA-approved drugs emphasizes a need to identify novel DLK-inhibitors. We screened NPAtlas (Natural products) and MedChemExpress (FDA-approved) libraries to identify potent ATP-competitive DLK inhibitors. ADMET analyses identified four compounds (two natural products and two FDA-approved) with favourable features. Subsequently, we performed molecular dynamics simulations to examine the binding-stability and ligand-induced conformational dynamics. Molecular mechanics Poisson Boltzmann surface area (MM-PBSA) calculations demonstrated CID139591660, dithranol, and danthron having greater affinity, while CID156581477 showed lower affinity than control sunitinib. PCA and network analysis results indicated structural and network alteration post-ligand binding. Furthermore, we identified an analogue of CID156581477 using the deep learning-based web server DeLA Drug which demonstrated a higher affinity than its parent compound and the control and identified several crucial interacting residues. Overall, our study provides significant theoretical guidance for designing potent novel DLK inhibitors and compounds that could emerge as promising drug candidates against DLK following laboratory validation. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) are gaining popularity for analyzing endoscopic images due to their many benefits. Since certain gastric polyps can lead to stomach cancer, it's crucial to detect and remove them accurately and promptly. CNN-based semantic segmentation helps by precisely outlining polyp areas, aiding endoscopists in identifying and treating them effectively. Despite the potential benefits, there is a scarcity of studies employing CNN for automated gastric polyp identification, particularly in the realm of semantic segmentation. Thus, we present groundbreaking research focused on segmenting gastric polyps in endoscopic images using CNNs. Various traditional semantic segmentation models, such as U-Net, DeepNet, SegNet, FuNet, and CustomNet (referred to as GISTNet), employing encoders like U-Net, ResNet50, MobileNetV2, or EfficientNet-B1, were constructed and scrutinized using a comprehensive dataset. Given the complexity of the problem and the multitude of criteria, selecting the most suitable CNN model poses a challenge. To address this, we propose an integrated evaluation approach that combines subjective considerations with objective data to identify the optimal CNN model. Our proposed network, CustomNet (GISTNet), employing ResNet as the encoder, emerged as the top performer according to our integrated evaluation method and was selected to construct the automated polyp segmentation system. This investigation underscores the clinical significance of semantic segmentation models in gastric polyp diagnosis and highlights the efficacy of the integrated evaluation approach in impartially selecting suitable models. Additionally, our research has the capacity to progress the identification techniques of gastric cancer, and the proposed evaluation methodology has implications for selecting diagnostic techniques based on mathematical models. [ABSTRACT FROM AUTHOR]

More than tools for managing physical and digital objects, museum collection management systems (CMS) serve as platforms for structuring, integrating, and making accessible the rich data embodied by natural history collections. Here we describe Arctos, a scalable community solution for managing and publishing global biological, geological, and cultural collections data for research and education. Specific goals are to: (1) Describe the core features and implementation of Arctos for a broad audience with respect to the biodiversity informatics principles that enable high quality research; (2) Highlight the unique aspects of Arctos; (3) Illustrate Arctos as a model for supporting and enhancing the Digital Extended Specimen concept; and (4) Emphasize the role of the Arctos community for improving data discovery and enabling cross-disciplinary, integrative studies within a sustainable governance model. In addition to detailing Arctos as both a community of museum professionals and a collection database platform, we discuss how Arctos achieves its richly annotated data by creating a web of knowledge with deep connections between catalog records and derived or associated data. We also highlight the value of Arctos as an educational resource. Finally, we present the financial model of fiscal sponsorship by a nonprofit organization, implemented in 2022, to ensure the long-term success and sustainability of Arctos. [ABSTRACT FROM AUTHOR]

The article explores the concept of "digital obscura," which refers to online user-produced audiovisual media that has been uploaded but remains unwatched or unnoticed. The author argues that studying this overlooked content can provide valuable insights into internet usage and platform functionalities. Digital obscura is difficult to find and often departs from generic conventions, offering a diverse range of content that may not conform to popular narratives or norms. The article emphasizes the need to slow down our media consumption and resist algorithmic recommendations in order to appreciate and learn from digital obscura. [Extracted from the article]

Societal Impact Statement: Madagascar's children are taught that their ancestors destroyed the vast ancient forest, giving rise to modern grasslands, a misleading myth that continues to undermine people's relationship with nature. Replacing this myth with a more nuanced and accurate narrative around grassy ecosystems is fundamental to building a more positive relationship between Madagascar's people and ecosystems, a process that is necessary to build modern conservation practice and environmental governance. The data we present are consistent with a pre‐human grass flora in the region, suggesting the local grassy ecosystems should not be written off as fully anthropogenic and undesirable but are instead in need of recognition and further research attention. Summary: Grassy ecosystems cover over a quarter of dry land, with mosaics of old‐growth and recently formed anthropogenic systems appearing superficially similar to an uninformed observer. Grasses are part of a deep web of historic relationships between grassy ecosystems and local people.We collect and compile the first list of grass species (Poaceae) in north‐west Madagascar in order to make an initial evidence assessment for a pre‐human grass flora. Field surveys and voucher specimen collections were carried out. Specimen identification was performed using morphological character comparison with reference specimens, literature and personal knowledge of grasses from other parts of Madagascar.Seventy species are recorded and assigned to 44 genera. The species list is compared with regional grass checklists from central and south‐central Madagascar. Thirty‐five grasses are unique to Mahajanga, comprising 19% of the multi‐region species pool analysed; the regional species richness is comparable to that of the highlands.The main subfamilies represented are the Panicoideae (64%) and Chloridoideae (29%). Seven species are endemic to Madagascar, reflecting their evolutionary origins on the island. Many of the species are found in multiple types of habitat and substrate, while others are restricted to limited areas and habitats, and local species richness varies significantly with habitat and soil. The main disturbance regimes driving species occurrence include fire and grazing, possibly partly reflecting similar disturbances prior to human arrival. The patterns observed in the grass flora of north‐western Madagascar are overall congruent with a pre‐human grass flora. [ABSTRACT FROM AUTHOR]

The use of malware in criminal investigations might be expanding. While "police hacking" is often publicized as used almost solely against pedophiles on the Dark Web, revelations from Israel on extensive police use of malware for a variety of criminal suspects might suggest that more intrusive forms of police hacking may emerge anywhere. Equipped with a wiretap warrant and malware, Israeli police forces can legally bypass encryption and directly obtain content and metadata from the device and its linked apps, turn on a suspect's camera or microphone, and essentially gain full access to the past and present of suspects. While the scope of police hacking in the U.S. is currently unknown, as this Article further argues, the legal framework that governs "search," access to stored communications and wiretaps could authorize such a practice much like in Israel, although it was never designed to do so. This obsolete framework to properly govern the use of malware by enforcement agencies must be updated and reconfigured. While reflecting on Frank Easterbrook's famous "law of the horse" argument, this Article suggests that trojan horses (malware) must be directly and individually regulated, especially in the realm of criminal law enforcement. This Article explores the history and legality of police hacking under the current legal framework. It then examines the impact of such practice on human rights, human liberties, and other externalities stemming from its use. It moves to propose a blueprint for policymakers on how to regulate police hacking properly, not before placing an almost absolute moratorium on its use until such regulation occurs. Police hacking should be allowed in some circumstances and under a rigorous, semi-technological oversight regime, as this Article suggests, but more importantly, such policymaking is crucial to draw a clear line for when it cannot be used. [ABSTRACT FROM AUTHOR]

In this article, we present a large-scale empirical analysis of the use of web storage in the wild.By using dynamic taint tracking at the level of JavaScript and by performing an automated classification of the detected information flows, we shed light on the key characteristics of web storage uses in the Tranco Top 10k. Our analysis shows that web storage is routinely accessed by third parties, including known web trackers, who are particularly eager to have both read and write access to persistent web storage information. We then deep dive in web tracking as a prominent case study: our analysis shows that web storage is not yet as popular as cookies for tracking purposes; however, taint tracking is useful to detect potential new trackers not included in standard filter lists. Moreover, we observe that many websites do not comply with the General Data Protection Regulation directives when it comes to their use of web storage. [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]

This paper presents built-up press brake-formed tub girders (PBTGs) as a new approach for the fabrication of resilient, short-span steel bridges. In contrast to existing PBTGs that are cold bent from a single steel plate, this is a built-up system in which webs are cold bent and bolted to separate, flat bottom and top flange plates. It offers enhanced versatility, as this is a "kit-of-parts" where different sizes of components can be used to achieve required capacities or span lengths (e.g., thicker bottom flanges for negative moment regions, deeper webs for longer spans). Using built-up sections joined by bolts provides internal member redundancy, as crack propagation between components is arrested. The objectives of this paper are to: (1) introduce the system including design details; (2) numerically demonstrate its feasibility through the finite element modeling of two demonstration bridges; and (3) investigate the internal redundancy by evaluating behavior when a flange or web is abruptly lost. Results indicate the viability and redundancy of the system for simple and two-span continuous bridges. [ABSTRACT FROM AUTHOR]

Nowadays, retinal fundus images (RFI) play a crucial role in the early and exact prediction of glaucoma. Glaucoma is a vision-threatening eye illness that primarily impairs the eye nerves, and the serious condition of glaucoma leads to permanent vision loss. Several computer vision-based techniques have been explored in past studies for glaucoma detection, but those have certain misclassification results and are not much focused on early stage detection. This work presents an ensemble deep network (Ensemble DeepNet) model for the early detection of glaucoma via retinal fundus images. Initially, the kernel bilateral filter (kernel BF) is employed to improve the clarity of retinal images and reduces image noise. Then, the brightness of the input image is modified using linear histogram transformation (LHT). Further, the segmentation of the optic disc (OD) and optic cup (OC) is performed using twostage thresholds U-network (Two-stage TUNet). Finally, a new ensemble DeepNet model is developed based on VGGNet, DenseNet, and CapsNet models using a weighted majority voting technique. The improved aquila optimization (IAO) algorithm is used to find the optimal weights for the ensemble DeepNet model. The proposed ensemble DeepNet model attains the highest of 99.45%, which shows that the proposed ensemble model is highly suitable for predicting glaucoma disease in an early stage. [ABSTRACT FROM AUTHOR]

The dark web is considered an expansion of the deep web, intentionally hidden from the surface web. It can only be accessed with a particular group of browsers that allow the user to stay anonymous while navigating the dark web. With the untraceable hidden layer of the Internet and the anonymity of the users associated with the dark web, several impressive cybercrimes have been reported. This paper aims to examine the impact of the dark web on society. The article systematically reviews relevant academic literature and books to understand how the dark web works and its societal effects. The study has found that the dark web is an enabler of several cybercrimes. Moreover, while governments and regulatory authorities have introduced strategic detection techniques on the dark web, cybercriminals are adaptive towards the strategies and, given time, will usually find ways to bypass such detection techniques. It is recommended that the regulatory authorities and cyber threat intelligence periodically review the detection techniques for effective monitoring. Furthermore, security agencies or forensic analysts should ensure that they are updated with the latest scientific knowledge on the safe management of the dark web by undertaking more training in cyber security. There is also a need for further research to focus on awareness campaigns about the dangers of the dark web. [ABSTRACT FROM AUTHOR]

Palmprint biometrics has a broad application prospect owing to non-intrusiveness, ease of image acquisition and stable textural pattern. Hand-crafted approaches are vulnerable to non-ideal palmprint images caused by uneven illumination, motion blur and noise contamination. Many researchers have designed excellent texture descriptors or/and advanced image pre-processing algorithms. Nevertheless, they are highly targeted at some specific data, less adaptable to the emerging data. In this paper, a semi-pretrained contactless palmprint recognition deep network is developed to achieve high accuracy and robustness. Semi-CPRN is composed of underlying network structure from ResNet-152 and the proposed assistant layers dedicated to high-performance palmprint recognition. The well-designed assistant layers enhance convolutional neural network to steadily extract the real palmprint features even from the degraded images without being deceived by the degradation factors. Besides, to better carry out the research on palmprint recognition in the open environment, we established a new contactless database HIT-NIST-V1 under natural scene. The comparative experiments on CASIA, IITD, PolyU3D/2D, Tongji and HIT-NIST-V1 illustrate that Semi-CPRN is comparable and superior to previously published state-of-the-art approaches. Simultaneously, CNN-based palmprint biometrics methods show significant robustness to motion blur, Gaussian noise, and salt and pepper noise. [ABSTRACT FROM AUTHOR]

Motivation Studying the genetic makeup of viruses and phages through genome analysis is crucial for comprehending their function in causing diseases, progressing medicine, tracing their evolutionary history, monitoring the environment, and creating innovative biotechnologies. However, accessing the necessary data can be challenging due to a lack of dedicated comparative genomic tools and viral and phage databases, which are often outdated. Moreover, many wet bench experimentalists may not have the computational proficiency required to manipulate large amounts of genomic data. Results We have developed VAPEX (Virus And Phage EXplorer), a web server which is supported by a database and features a user-friendly web interface. This tool enables users to easily perform various genomic analysis queries on all natural viruses and phages that have been fully sequenced and are listed in the NCBI compendium. VAPEX therefore excels in producing visual depictions of fully resolved synteny maps, which is one of its key strengths. VAPEX has the ability to exhibit a vast array of orthologous gene classes simultaneously through the use of symbolic representation. Additionally, VAPEX can fully analyze user-submitted viral and phage genomes, including those that have not yet been annotated. Availability and implementation VAPEX can be accessed from all current web browsers such as Chrome, Firefox, Edge, Safari, and Opera. VAPEX is freely accessible at https://archaea.i2bc.paris-saclay.fr/vapex/. [ABSTRACT FROM AUTHOR]

As the center of the financial market, the stock market is popular with the public attention of investors. It is of great significance for investors that an effective analytic method of stock public opinion is proposed. As the main communication platform, the forum not only provides the investors with investment information but also comments related to the stock market. In view of the defects of text emotions and investment problems, this paper proposes a framework based on web crawler and deep learning technologies including one-dimensional convolutional neural networks (1DCNN) and long short-term memory (LSTM), to evaluate the stock market volatility. Among them, the extracted features include not only the stock price but also the text information. Firstly, we develop the crawler technology to grab large-scale text data from the internet and they are manually labeled their emotions by analyzing the relevant financial knowledge. Secondly, as the character-level text classification method, the 1DCNN is designed for text sentiment classification to detect the reliability of text annotation. Finally, considering the time sequence of price and the continuity of post influence, the emotional and technical features are combined to estimate the fluctuation of the stock market in different industries by the LSTM model. We test four evaluation indexes, the classification accuracy of the model is 74.38%, the accuracy rate is 76.83%, the recall rate is 70%, and the F1 value is 72.8%. The results show that the combination of characteristics of internet public opinion more effectively evaluates the changes in the stock market. [ABSTRACT FROM AUTHOR]

Due to the massive size of the hidden web, searching, retrieving and mining rich and high-quality data can be a daunting task. Moreover, with the presence of forms, data cannot be accessed easily. Forms are dynamic, heterogeneous and spread over trillions of web pages. Significant efforts have addressed the problem of tapping into the hidden web to integrate and mine rich data. Effective techniques, as well as application in special cases, are required to be explored to achieve an effective harvest rate. One such special area is atmospheric science, where hidden web crawling is least implemented, and crawler is required to crawl through the huge web to narrow down the search to specific data. In this study, an intelligent hidden web crawler for harvesting data in urban domains (IHWC) is implemented to address the relative problems such as classification of domains, prevention of exhaustive searching, and prioritizing the URLs. The crawler also performs well in curating pollution-related data. The crawler targets the relevant web pages and discards the irrelevant by implementing rejection rules. To achieve more accurate results for a focused crawl, ICHW crawls the websites on priority for a given topic. The crawler has fulfilled the dual objective of developing an effective hidden web crawler that can focus on diverse domains and to check its integration in searching pollution data in smart cities. One of the objectives of smart cities is to reduce pollution. Resultant crawled data can be used for finding the reason for pollution. The crawler can help the user to search the level of pollution in a specific area. The harvest rate of the crawler is compared with pioneer existing work. With an increase in the size of a dataset, the presented crawler can add significant value to emission accuracy. Our results are demonstrating the accuracy and harvest rate of the proposed framework, and it efficiently collect hidden web interfaces from large-scale sites and achieve higher rates than other crawlers. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification. [ABSTRACT FROM AUTHOR]

Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today's challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments. [ABSTRACT FROM AUTHOR]

The advent of metabarcoding (metaB) in aquatic ecology has provided a huge amount of information on plankton biodiversity worldwide. However, the large datasets obtained with that approach are still partially explored, especially for what concerns the study of trophic interactions and food webs. In this study, we analysed a metaB time series from the Long‐Term Ecological Research station MareChiara (LTER‐MC) in the Gulf of Naples, Mediterranean Sea, Italy, to describe the link between plankton diversity and food‐web structure. We derived co‐occurrence networks from metaB time series, identified putative trophic interactions among co‐occurrences based on biological information (body size and trophic habit) available for planktonic organisms detected by metaB, and converted co‐occurrence networks into conceptual models of food webs. The latter showed structural properties resembling ecological processes, because network modularity (the presence of semi‐independent sub‐networks) paralleled trophic hierarchy (the dimensional difference between predator and prey). We also analysed the role of planktonic organisms in maintaining network modularity. The largest predators occupied distinct modules, suggesting niche partitioning, whereas the smallest preys worked as fundamental connectors between larger predators (and different modules). Overall, the presence of trophic hierarchy and modularity shown herein supports the view of the high ecological resilience of plankton, pursued via food‐web rewiring, to environmental shifts. [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]

Reconnaissance and monitoring activities of actors on the Internet are becoming indispensable security tools for governments around the world. The Deep Web as part of the Internet protects actors. However, cheap and reliable encryption and online identity protection have become attractive to state and non-state actors in the cyber domain. Unfortunately, part of the Deep Web, the Dark Web is now offering to training terrorists, and trading arms and illicit substances. The Dark Web represents a communication and information systems infrastructure where actors are anonymous and the services are adaptable to the demands of geopolitical circumstances. This paper analyzes the ways of using the Dark Web and its impact on the information environment since the global war on terrorism began, through a period when humanity struggled with Covid-19 pandemic to the war in Ukraine. [ABSTRACT FROM AUTHOR]

Abazari, Arash Hegel's Ontology of Power: The Structure of Social Domination in Capitalism Cambridge Cambridge University Press 2020 1 218 The relationship between Hegel's logic and his political philosophy is a major point of contention within contemporary Hegelian scholarship. Moreover, as Abazari rightly recognizes, Hegel draws specifically on the categories of the Doctrine of Essence to characterize the modern market sphere, where individuals are "altogether on the level of semblance" and subjected to an alien power (§ 188A). If we accept that "Hegel's logic is the reconstruction of historical actuality in terms of the most general categories of thought", he writes, "we will have to accept that Marx's view [...] is in fact very close to Hegel's" (8). [Extracted from the article]

Development of a handwriting classifier using deep learning approach that can classify handwritten numbers and digits on the web. It is a deep-learning based system that uses modern algorithms and focuses on creating a portable web application that aims to classify handwritten numbers powered by the MNIST dataset. [ABSTRACT FROM AUTHOR]

Enhanced richness and size of the data on the web paves the path for increased online services, supporting the sophisticated usage of heterogeneous complex tasks by users. As deep web online services are increasing, several efficient techniques to explore the location of deep web interfaces are also leveraged thereby providing better support in user information exploration on the web concerning queries and the user clicks on web-related search data. This searching process provides an improved experience to users using crawling websites and searches rank with titles and links. The main problem behind search data on the web is organizing user search information based on their preferences into dynamic query formations and data coming from associated web links in an automated way on web services on high-quality sparse data. Vast amounts of data with the Dynamic nature of web interfaces explore or achieve high efficiency and coverage of all deep web interfaces that turn out to be a challenging issue. To handle such issues, we propose and introduce a novel and efficient two-phase deep learning data crawler framework (NTPDCF). The first phase initiates in gathering accurate and highly relevant links using the search engine, and the second phase explores fast and in-site relevant website links using adaptive site ranking. The approach focuses on drilling relevant site data and top-k ranking with different relations based on dynamic features with user preferences in single- and multi-query formation with adaptive weight features. The method promises to visualize improved results with efficient data exploration over the traditional approach concerning real-time defense and e-commerce related to web-based services. [ABSTRACT FROM AUTHOR]

The metazoan parasite community and the stomach contents of the nursehound shark Scyliorhinus stellaris from the Gulf of Naples (central Mediterranean Sea) were studied. The nursehound shark harboured a poor parasite community composed of a species of gill monogenean (Hexabothrium appendiculatum) and three intestinal cestode taxa (Acanthobothrium coronatum and two unidentified species of the genera Yamaguticestus and Scyphophyllidium), all represented by adult stages. Hosts were mostly parasitized by individuals of A. coronatum, which was the most abundant species and contributed to almost 80% of the total number of parasites found. Conversely, other trophically transmitted parasites (i.e., Yamaguticestus sp. and Scyphophyllidium sp.) showed low prevalence and abundance. The parasite infracommunity was poor, showing low values of species richness, total mean abundance, and diversity indices. Overall, 52 prey items belonging to 13 taxa were identified in the stomach contents. Cephalopods were the most important prey items (represented by nine taxa) and the most diverse and abundant group. In the multivariate space provided by a principal component of mixed data (PCAmix), nursehound sharks distributed along two main axes, related to individual traits (first axis) and stomach contents (including empty ones, second axis). A logistic regression based on the first two axes of the PCAmix showed a significant influence of host individual traits and, to a lesser extent, of stomach contents, regarding the probability of being infected by A. coronatum. Alongside specific traits already associated with parasites transmission, our results highlight the importance of cephalopods in transferring cestode infections through trophic web interactions in the top‐predator nursehound shark. [ABSTRACT FROM AUTHOR]

Deep web collection means entering a query in a search form and collecting response results. It is estimated that the information possessed by the deep web has about 450 to 550 times more information than the statically constructed surface web. The static method does not show the changed information until the web page is refreshed, but the dynamic web page method updates the necessary information in real time and provides real-time information without reloading the web page, but crawler has difficulty accessing the updated information. Therefore, there is a need for a way to automatically collect information on these deep webs using a crawler. Therefore, this paper proposes a method of utilizing scripts as general links, and for this purpose, an algorithm that can utilize client scripts like regular URLs is proposed and experimented. The proposed algorithm focused on collecting web information by menu navigation and script execution instead of the usual method of entering data into search forms. [ABSTRACT FROM AUTHOR]