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This chapter explores the significance of blockchain technology in protecting data for intelligent applications across various industries. Blockchain is a distributed ledger that ensures the immutability and security of transactions. Given the increasing need for security measures in industries, understanding blockchain technology is crucial for preparing for its future applications.

This chapter aims to examine the use of blockchain technology across industries and presents a compilation of existing and upcoming blockchain technologies for intelligent applications. The methodology involves reviewing research to understand the security needs of different industries and providing an overview of methods used to enhance multi-institutional and multidisciplinary research in areas like the financial system, smart grid, and transportation system.

The findings highlight the benefits of blockchain networks in providing transparency, trust, and security for industries. The Responsible Sourcing Blockchain Network (RSBN) is an example that utilizes blockchain's decentralized ledger to track sustainable sourcing from mine to final product. This information can be shared with auditors, corporate governance organizations, and customers.

The practical implications of this chapter are significant, serving as a valuable resource for industries concerned with identity privacy, traceability, immutability, transparency, auditability, and security. Understanding and implementing blockchain technology can address the growing need for secure and intelligent applications, ensuring data protection and enhancing trust in various sectors.

While the world grappled with the COVID-19 pandemic and its externalities, scientists have recommended that the global community brace against potential future pandemics. The need to build resilient systems has never been this urgent. The world, especially emerging economies, faces acute food insecurity, high food prices, failing health infrastructure and rampant misinformation spread, among others. Since blockchain technology (BCT) has been discussed in the supply chain resilience context, and it offers the potential to develop resilient systems, we aim to outline the potential of BCT to help build resilience against ongoing and future pandemics. Mainly, we focus on BCT for healthcare management, disruption management of food supply chains, human resource management, modern education and certification and governance and administration.

This study aims to proposed that blockchain helps the organization improve supply chain (SC) performance by improving integration, agility and security through real-time information sharing, end-to-end visibility, transparency, data management, immutability, irrevocable information and cyber-security platforms.

This study has made an initial effort toward proposing a framework that shows the problems and challenges for the O&G SC under its segments (upstream, midstream and downstream) and provides the interlink among blockchain properties for SCM problems. SC managers were selected for survey questionnaires from the Pakistan O&G industries.

This study analyzes the impact of blockchain-enabled SC on firm performance with an understanding of the SC robustness capabilities as a mediator. The result revealed that the SC manager believes that the blockchain-enabled SC has a positive and significant on firm performance and robustness capabilities.

Blockchain technology is reflected as high-tech to support the firm process, responses and methods. The technology helps eliminate bottlenecks, avoid uncertainties and improve decision-making, leading to improved SC functions. This study guides managers about the potential problems of existing SC and how blockchain solves SC problems more effectively.

The oil and gas (O&G) sectors are neglected by researchers, and there are limited studies on O&G supply chain management (SCM). Additionally, no empirical evidence suggests implementing blockchain for O&G as a solution for potential problems. Furthermore, present the roadmap to other industries those having complex SC networks for the implication of blockchain to improve the SC performance.

This paper aims to use hybrid manufacturing (HM) to overcome several drawbacks of material extrusion three-dimensional (3D) printers, such as low dimension ranging from 0.2 to 0.5 µm, resulting in a noticeable staircase effect and elevated surface roughness.

Subtractive manufacturing (SM) through computer numerical control milling is renowned for its precision and superior surface finish. This study integrates additive manufacturing (AM) and SM into a single material extrusion 3D printer platform, creating a HM system. Two sets of specimens, one exclusively printed and the other subjected to both printing and milling, were assessed for dimension accuracy and surface roughness.

The outcomes were promising, with postmilling accuracy reaching 99.94%. Significant reductions in surface roughness were observed at 90° (93.4% decrease from 15.598 to 1.030 µm), 45° (89% decrease from 26.727 to 2.946 µm) and the face plane (71% decrease from 12.176 to 3.535 µm).

The 3D printer was custom-built based on material extrusion and modified with an additional milling tool on the same gantry. An economic evaluation based on cost-manufacturing demonstrated that constructing this dual-function 3D printer costs less than US$560 in materials, offering valuable insights for researchers looking to replicate a similar machine.

The modified general 3D printer platform offered an easy way to postprocessing without removing the workpiece from the bed. This mechanism can reduce the downtime of changing the machine. The proven increased dimension accuracy and reduced surface roughness value increase the value of 3D-printed specimens.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

The purpose of this study is to increasing the gauge factor, reducing the hysteresis error and improving the stability over cyclic deformations of a conductive polylactic acid (CPLA)-based 3D-printed strain sensor by modifying the sensing element geometry.

Five different configurations, namely, linear, serpentine, square, triangular and trapezoidal, of CPLA sensing elements are printed on the thermoplastic polyurethane substrate material individually. The resistance change ratio of the printed sensors, when loaded to a predefined percentage of the maximum strain values over multiple cycles, is recorded. Finally, the thickness of substrate and CPLA and the included angle of the triangular strain sensor are evaluated for their influences on the sensitivity.

The triangular configuration yields the least hysteresis error with high accuracy over repeated loading conditions, because of its uniform stress distribution, whereas the conventional linear configuration produces the maximum sensitivity with low accuracy. The thickness of the substrate and sensing element has more influence over the included angle, in enhancing the sensitivity of the triangular configuration. The sensitivity of the triangular configuration exceeds the linear configuration when printed at ideal sensor dimensional values.

The 3D printing parameters are kept constant for all the configurations; rather it can be varied for improving the performance of the sensor. Furthermore, the influences of stretching rate and nozzle temperature of the sensing material are not considered in this work.

The sensitivity and accuracy of CPLA-based strain sensor are evaluated for modification in its geometry, and the performance metrics are enhanced using the regression modelling.

Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC.

A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to confirm the proposed relationships.

The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT.

This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance.

Effective performance of a direct ethanol fuel cell (FC) stack depends on the satisfactory operation of its individual cells where it is always challenging to manage the temperature gradient, water flow and distribution of reactants. In that, the design of the bipolar fuel flow path plate plays a vital role in achieving the aforementioned parameters. Further, the bipolar plates contribute 80% of the weight and 30%–40% of its total cost. Aim of this study is to enhance the efficiency of fuel to energy conversion and to minimize the overall cost of production.

The authors have specifically designed, simulated and fabricated a standard 2.5 × 2.5 cm² active area proton exchange membrane (PEM) FC flow path plate to study the performance by varying the flow fields in a single ladder, double ladder and interdigitated and varying channel geometries, namely, half curve, triangle and rectangle.

Using the 3D PEMFC model and visualizing the physical and electrochemical processes occurring during the operation of the FCs resulted in a better-performing flow path plate design. It is fabricated by using additive manufacturing technology. In addition, the assembly of the full cell with the designed flow path plate shows about an 11.44% reduction in total weight, which has a significant bearing on its total cost as well as specific energy density in the stack cell.

Simultaneous optimization of multiple flow path parameters being carried out for better performance is the hallmark of this study which resulted in enhanced energy density and reduced cost of device production.

This study aims to examine the impact of specific printing factors, such as layer height, line width and build orientation, on the overall quality of fused filament fabrication (FFF) 3D printed structures. The project also intends to use response surface methodology (RSM) to maximize ultimate tensile strength (UTS) while lowering surface roughness and printing time.

This study used an FFF printer to fabricate samples of polylactic acid (PLA), which were then subjected to assessments of tensile strength and surface roughness. A tensile test was conducted under standardized conditions according to the ASTM D638 standard test method using the AG-50 kN Shimadzu Autograph. The Mitutoyo Surftest SJ-210, which utilizes a needle-tipped inductive method, was used to determine surface roughness. RSM was used for optimization.

This work provides useful insights into how the printing parameters affect FFF 3D printed structures, which may be used to optimize the printing process and improve PLA-based 3D printed products' qualities. The determined optimal values for building orientation, layer height and line width were 0°, 0.1 mm and 0.6 mm, respectively. The total desirability value of 0.80 implies desirable outcomes, and good agreement between experimental and projected response values supports the suggested models.

Previous RSM studies for 3D printing parameter optimization focused on mechanical properties or surface aspects, however, few examined multiple responses and their interactions. This study emphasizes the relevance of FFF parameters like line width, which are often overlooked but can dramatically impact printing quality. Mechanical properties, surface quality and printing time are integrated to comprehend optimization holistically.

Purpose: The prominence of quality education for building sustainable development is undeniable and is distinctly pointed out in 1 of the 14 sustainable development goals (SDGs). In the same context, this study intends to investigate the role of university commitment, perceived organisational prestige, student satisfaction, and perceived employability in enhancing sustainability in higher education.

Need of the Study: To evaluate how student satisfaction mediates the relationship between university commitment, perceived organisational prestige, and perceived employability with sustainable university institutes.

Methodology: An adapted questionnaire was used in this study to capture the perception of 458 management graduates selected through the purposive sampling method. Partial least squares structural equation modelling (PLS-SEM) technique was used to analyse the data with the help of Smart PLS software.

Findings: The results of this study show that student satisfaction is the strongest predictor of sustainable university institutes. University commitment was found to lead to student satisfaction significantly. Furthermore, student satisfaction wasn’t found to play the role of mediator in the proposed model.

Practical Implications: This study aims to fulfil theoretical, research, and management implications for students, higher education institutes (HEIs), and policymakers. HEIs are recommended to instil university commitment, perceived organisational prestige and student satisfaction via various practices and amendments in their curriculum. Students are recommended to enhance their perceived employability to achieve career sustainability.