Superficial siderosis of the central nervous system results from subpial hemosiderin deposition due to chronic low‐grade bleeding into the subarachnoid space. The confluent and marginal subpial ...hemosiderin is best appreciated on iron‐sensitive magnetic resonance imaging sequences. With widespread use of magnetic resonance imaging, the disorder is increasingly being recognized, including in asymptomatic individuals. Gait ataxia, often with hearing impairment is a common clinical presentation. A clinical history of subarachnoid hemorrhage is generally not present. A macrovascular pathology is generally not causative. The most common etiology is dural disease, often dural tears. Prior or less commonly ongoing symptoms of craniospinal hypovolemia may be present. Common etiologies for dural tears include disc disease and trauma, including surgical trauma. Patients with dural tears due to herniated and calcified discs often have a ventral intraspinal fluid collection due to cerebrospinal fluid leak. A precise identification of the dural tear relies on multimodality imaging. It has been speculated that chronic bleeding from fragile blood vessels around the dural tear may be the likely underlying mechanism. Surgical correction of the bleeding source is a logical therapeutic strategy. Clinical outcomes are variable, although neuroimaging evidence of successful dural tear repair is noted. The currently available data regarding use of deferiprone in patients with superficial siderosis is insufficient to recommend its routine use in patients. ANN NEUROL 2021;89:1068–1079
Display omitted
•We present a comprehensive review on blockchain-based 5G-enabled IoT and discussed its potential industrial applications in detail.•We also discuss various research challenges for ...integration of blockchain with 5G-enabled IoT for industrial automation.•Finally, this paper bridges the gap between the scalability, interoperability, and other research challenges.
Internet-of-Things (IoT) has made ubiquitous computing a reality by extending Internet connectivity in various applications deployed across the globe. IoT connect billions of objects together for high speed data transfer especially in 5G-enabled industrial environment during information collection and processing. Most of the issues such as access control mechanism, time to fetch the data from different devices and protocols used may not be applicable infor future applications as these protocols are based upon a centralized architecture. This centralized architecture may have a single point of failure alongwith the computational overhead. So, there is a need for an efficient decentralized access control mechanism for device-to-device (D2D) communication in various industrial sectors IoT-enabled industrial automation. In such an environment, security and privacy preservation are major concerns as most of the solutions are based upon the centralized architecture. To mitigate the aforementioned issues, in this paper, we present an in-depth survey of state-of-the-art proposals having 5G-enabled IoT as a backbone for blockchain-based industrial automation for the applications such as-Smart city, Smart Home, Healthcare 4.0, Smart Agriculture, Autonomous vehicles and Supply chain management. From the existing proposals, it has been observed that blockchain can revolutionize most of the current and future industrial applications in different sectors by providing a fine-grained decentralized access control. Various transactions and database logs can be traced efficiently using blockchain for consistency and preivacy preservation in the aforementiioned industrial sectors. The open issues and challenges of 5G-enabled IoT for blockchain-based Industrial automation are also analyzed in the text. Finally, a comparison of existing proposals with respect to various parameters is presented which allows the end users to select one of the proposals in comparison to its merits over the others.
Software-defined network (SDN) and network function virtualization (NFV) have entirely changed the way internetwork backhaul should be utilized and behaved for virtualized service provisioning. ...Several benefits have been observed in multiple domains of applications that has used SDN and NFV in integrated way. Thus, SDN/NFV paradigm has been investigated to seek whether network services could be efficiently delivered, managed, and disseminated to the end users. Internet of Things (IoT) is justifiably associated with the SDN/NFV augmentation to make this task enriched. However, factors related to edge-cloud communication and network services have not been effectively mitigated until now. In this paper, we present an in-depth, qualitative, and comprehensive systematic review to find the answers of following research questions, such as, (i) how does state-of-the-art SDN/NFV architecture look like, (ii) how to solve next generation cellular services via architecture involvement, (iii) what type of application/test-bed need to be studied, and (iv) security framework should be catered. We further, elaborate various key issues and challenges in the existing architecture mitigation for SDN/NFV integration to the IoT-based edge-cloud oriented network service provisioning. Future directions are also prescribed to support fellow researchers to improve existing virtualized service scenario. Lessons learned after performing comparative study with other survey articles dictates that our work presents timely contribution in terms of novel knowledge toward understanding of formulating SDN/NFV virtualization services under the aegis of IoT-centric edge-cloud scenario.
In the last few years, there has been an exponential increase in the usage of the autonomous vehicles across the globe. It is due to an exponential increase in the popularity and usage of the ...artificial intelligence techniques in various applications. Traffic flow predication is important for autonomous vehicles using which they decide their itinerary and take adaptive decisions (for example, turn let or right, move straight, lane change, stop, or accelerate) with respect to their surrounding objects. From the existing literature, it has been observed that research on autonomous vehicles has shifted from the traditional statistical models to adaptive machine learning techniques. However, existing machine learning models may not be directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles. So, with focus on these issues, in this article, we explore various deep learning models for traffic flow prediction in autonomous vehicles and compared these models with respect to their applicability in modern smart transportation systems. Various parameters are chosen to have a relative comparison among different deep learning models. Moreover, challenges and future research directions are also discussed in the article.
The digitalization and massive adoption of advanced technologies in the automotive industry not only transform the equipment manufacturer's operating mode, but also change the current business ...models. The increased adoption of autonomous cars is expected to disrupt government regulations, manufacturing, insurance, and maintenance services. Moreover, providing integrated, personalized, and on-demand services have shared, connected, and autonomous cars in the smart city for a sustainable ecosystem. To address these issues in this paper, we propose a blockchain-based distributed framework for the automotive industry in the smart city. The proposed framework includes a novel miner node selection algorithm for the blockchain-based distributed network architecture. To evaluate the feasibility of the proposed framework, we simulated the proposed model on a private Ethereum blockchain platform using captured dataset of mined blocks from litecoinpool.org . The simulation results show the proof-of-concept of the proposed model that can be used for wide range of future smart applications.
Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image ...processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.
Display omitted
The present work aims to investigate applicability of antisolvent precipitation method for preparation of nanosized curcumin and to control their characteristics by determining the ...influence of process and solvents on solid-state properties of curcumin nanoparticles. Effects of different experimental parameters on particle size were investigated using dynamic light scattering. Particle morphology was studied using SEM. Drug content in stabilized nanoparticles was determined using HPLC. Residual moisture content after lyophilisation was determined using Karl Fischer method and solid state properties were investigated using DSC, TGA, FTIR and powder-XRD. The resulting product showed a high drug load and contained the drug in amorphous form. The particle diameters of prepared curcumin nanoparticles were found in the range of 100–200nm. In vitro drug release studies indicated a sustained release profile of curcumin from the nanoparticles. Antisolvent precipitation produced amorphous curcumin nanoparticles whose size and morphology could be controlled using gelatine as stabilizer. Lyophilized curcumin nanoparticles with d-sorbitol as lyoprotectant possessed good redispersibility and showed up to 4 times faster in vitro curcumin release rate than that of unprocessed curcumin. Stability tests (at 2–8°C and ambient conditions) indicated that the product was stable for up to 6 months of storage.
AbstractEnsuring success in the construction business is the aim of the owner/top management of the construction organizations. Previous research indicates that the presence of certain factors in an ...organization make it more successful compared with the organizations without those factors. Because the construction business is one of the riskiest businesses in the world, identification of such factors and adoption of these factors in the work ethic of the company is of vital importance for the owner/top management of the construction organizations. This study aims to test the hypothesis that success factors influence the success of the construction organization and also explores the impact of these factors on the success of the construction organization. The research methodology involved seeks responses from experts in the construction industry through a questionnaire survey. A total of 106 industry experts from 90 different construction organizations operating in India participated in the survey. Structural equation modeling (SEM) was used to test the hypothesized positive relationships between six success factors, and the success of the construction organization is measured against five performance factors. The findings of the study indicate that top management competence is the most important factor followed by “experience and performance.” The study could provide an excellent value addition to the professionals working in the field of construction management. Further, the results would enable professionals to focus on fewer factors rather than attending numerous factors for optimum result.
In recent years, rapid technological advancements in smart devices and their usage in a wide range of applications exponentially increases the data generated from these devices. So, the traditional ...data analytics techniques may not be able to handle this extreme volume of data known as Big Data (BD) generated by different devices. However, this exponential increase of data opens the doors for the different type of attackers to launch various attacks by exploiting various vulnerabilities (SQL injection, OS fingerprinting, malicious code execution, etc.) during data analytics. Motivated from the aforementioned discussion, in this paper, we explored Machine Learning (ML) and Deep Learning (DL)-based models and techniques which are capable off to identify and mitigate both the known as well as unknown attacks. ML and DL-based techniques have the capabilities to learn from the traffic pattern using training and testing datasets in the extensive network domains to make intelligent decisions concerning attack identification and mitigation. We also proposed a DL and ML-based Secure Data Analytics (SDA) architecture to classify normal or attack input data. A detailed taxonomy of SDA is abstracted into a threat model. This threat model addresses various research challenges in SDA using multiple parameters such as-efficiency, latency, accuracy, reliability, and attacks launched by the attackers. Finally, a comparison of existing SDA proposals with respect to various parameters is presented, which allows the end users to select one of the SDA proposals in comparison to its merits over the others.
