Blockchain technology has gained considerable attention, with an escalating interest in a plethora of numerous applications, ranging from data management, financial services, cyber security, IoT, and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure healthcare data management. Also, blockchain is reforming the traditional healthcare practices to a more reliable means, in terms of effective diagnosis and treatment through safe and secure data sharing. In the future, blockchain could be a technology that may potentially help in personalized, authentic, and secure healthcare by merging the entire real-time clinical data of a patient’s health and presenting it in an up-to-date secure healthcare setup. In this paper, we review both the existing and latest developments in the field of healthcare by implementing blockchain as a model. We also discuss the applications of blockchain, along with the challenges faced and future perspectives.

With the widespread of blockchain technology, preserving the anonymity and confidentiality of transactions have become crucial. An enormous portion of blockchain research is dedicated to the design and development of privacy protocols but not much has been achieved for proper assessment of these solutions. To mitigate the gap, we have first comprehensively classified the existing solutions based on blockchain fundamental building blocks (i.e., smart contracts, cryptography, and hashing). Next, we investigated the evaluation criteria used for validating these techniques. The findings depict that the majority of privacy solutions are validated based on computing resources i.e., memory, time, storage, throughput, etc., only, which is not sufficient. Hence, we have additionally identified and presented various other factors that strengthen or weaken blockchain privacy. Based on those factors, we have formulated an evaluation framework to analyze the efficiency of blockchain privacy solutions. Further, we have introduced a concept of privacy precision that is a quantifiable measure to empirically assess privacy efficiency in blockchains. The calculation of privacy precision will be based on the effectiveness and strength of various privacy protecting attributes of a solution and the associated risks. Finally, we conclude the paper with some open research challenges and future directions. Our study can serve as a benchmark for empirical assessment of blockchain privacy.

Semantic segmentation for accurate visual perception is a critical task in computer vision. In principle, the automatic classification of dynamic visual scenes using predefined object classes remains unresolved. The challenging problems of learning deep convolution neural networks, specifically ResNet-based DeepLabV3+ (the most recent version), are threefold. The problems arise due to (1) biased centric exploitations of filter masks, (2) lower representational power of residual networks due to identity shortcuts, and (3) a loss of spatial relationship by using per-pixel primitives. To solve these problems, we present a proficient approach based on DeepLabV3+, along with an added evaluation metric, namely, Unified DeepLabV3+ and $S^{3}core$, respectively. The presented unified version reduced the effect of biased exploitations via additional dilated convolution layers with customized dilation rates. We further tackled the problem of representational power by introducing non-linear group normalization shortcuts to solve the focused problem of semi-dark images. Meanwhile, to keep track of the spatial relationships in terms of the global and local contexts, geometrically bunched pixel cues were used. We accumulated all the proposed variants of DeepLabV3+ to propose Unified DeepLabV3+ for accurate visual decisions. Finally, the proposed $S^{3}core$ evaluation metric was based on the weighted combination of three different accuracy measures, i.e., the pixel accuracy, IoU (intersection over union), and Mean BFScore, as robust identification criteria. Extensive experimental analysis performed over a CamVid dataset confirmed the applicability of the proposed solution for autonomous vehicles and robotics for outdoor settings. The experimental analysis showed that the proposed Unified DeepLabV3+ outperformed DeepLabV3+ by a margin of 3% in terms of the class-wise pixel accuracy, along with a higher $S^{3}core$, depicting the effectiveness of the proposed approach.

Designing an effective drilling mud is a critical aspect of the drilling process. A well-designed drilling mud should not only provide efficient mud hydraulics but also fulfill three important functions: enhancing mud rheology, inhibiting hydrate formation in deepwater drilling, and suppressing shale swelling when drilling through shale formations. Achieving these functions often requires the use of various additives, but these additives are often expensive, non-biodegradable, and have significant environmental impacts. To address these concerns, researchers have explored the potential applications of ionic liquids and deep eutectic solvents in drilling mud design, which have shown promising results. However, an even more environmentally friendly alternative has emerged in the form of natural deep eutectic solvents (NADES). This research focuses on an in-house-prepared NADES based on calcium chloride and glycerine, with a ratio of 1:4, prepared at 60 °C, and utilizes it as a drilling mud additive following the API 13 B-1 standards and checks its candidacy as a rheology modifier, hydrates, and shale inhibitor. The findings of the study demonstrate that the NADES-based mud significantly improves the overall yield point to plastic viscosity ratio (YP/PV) of the mud, provides good gel strength, and inhibits hydrate formation by up to 80%. Additionally, it has shown an impressive 62.8% inhibition of shale swelling while allowing for 84.1% improved shale recovery. Moreover, the NADES-based mud exhibits a 28% and 25% reduction in mud filtrate and mud cake thickness, respectively, which is further supported by the results of XRD, zeta potential, and surface tension. Based on these positive outcomes, the calcium chloride–glycerine NADES-based mud is recommended as a versatile drilling mud additive suitable for various industrial applications. Furthermore, it presents a more environmentally friendly option compared to traditional additives, addressing concerns about cost, biodegradability, and environmental impact in the drilling process for an ultimate global impact.

Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.

Halal food is gaining attention among Muslims and non-Muslims alike due to its nature of ensuring food is free from any impurities or contamination and hygienically prepared. The growing demand for Halal food has resulted in several food-producing companies to certify their products as Halal. However, with existing supply chains, there is no authenticity of these products being Halal. To ensure Halal food authenticity, the technology of blockchain is proposed as a viable solution. In this chapter, the applicability and usability of blockchain technology in food supply chain management systems is studied and highlighted. The study depicts that how trackability and traceability of the blockchain networks can effectively aid in maintaining the Halal integrity of food products by presenting various use cases. Technological shift for food supply chains over blockchains will result in more transparent, secure, and resilient supply chains. This will bring variety of health and economic benefits to food producing business and consumers.