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Healthcare has been facing rising challenges in recent years. To mitigate these issues, an appreciable amount of effort has been invested in studies about Healthcare 4.0. Despite the recognized importance of this topic, its recentness and multidisciplinary character are obstacles to its precise understanding. In this light, this paper aims to provide a comprehensive view of the current development of Healthcare 4.0.

We systematically reviewed the literature, which resulted in 130 papers retrieved from the Web of Science and Scopus databases. Quantitative and qualitative analyses were carried out using this sample.

As a result of the quantitative analysis, we notice an increasing trend in health-related studies, notwithstanding its novelty. India is also recognized as the leading reference on the subject, as it is the country with more papers in the sample and the most influential authors. On the other hand, through qualitative analysis, an extensive review and analysis of the technologies and applications of Healthcare 4.0 is presented, along with a discussion of its underexplored areas.

The results of this paper provide valuable knowledge to guide and encourage further analysis on this topic, including recommendations for future research directions.

Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.

The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one.

These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India.

The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.

While off-site construction (OSC) offers a promising solution to many problems plaguing traditional construction (e.g. low productivity, waste and safety risks), a lack of standards and knowledge about OSC, especially regarding disputes, hinders its wider adoption. This study aims to address this gap by identifying and analyzing the importance levels of technical, managerial and external disputes specific to OSC projects.

Three steps methodology was employed in the study. Focus group discussion (FGD) technique was used to identification and finalize dispute causes found from literature and collect data for the next step. In the study, two multi-criteria decision-making MCDM (methods) [a hybrid approach-Pythagorean fuzzy analytic hierarchy process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)] were employed. While Pythagorean fuzzy AHP was used to calculate the weights of criteria, fuzzy TOPSIS analysis was used to calculate the weights of main and sub-dispute causes. Instead of using the classical AHP method, the Pythagorean fuzzy AHP method was employed due to its superiority in capturing the inherent uncertainty and ambiguity of decision-makers, giving flexibility to decision-makers with linguistic variables instead of expecting exact evaluation scores and flexibility in the integration with other methods. During the analysis of the weights of the main and sub-dispute causes, the fuzzy TOPSIS method was preferred. The fuzzy TOPSIS method involves a quicker and more straightforward decision-making process. Also, the fuzzy TOPSIS method allows the consideration of numerous alternatives and evaluation criteria and uncertainty in the decision-making process.

The analysis reveals that technical disputes pose the biggest challenge in off-site construction compared to managerial or external disputes. Specifically, “late completion, delivery and installation of components” emerged as the most significant technical dispute. Within managerial disputes, “poor planning and management of the project” ranked highest, while “the complexity of legal expressions” was the most prominent external dispute factor.

In the literature, three studies offer some insight on OSC disputes by analyzing the litigation cases. Nonetheless, the results can be misleading, because some disputes can be resolved before the litigation process. Therefore, the study findings can aid in foreseeing technical, managerial and external factors and in generating robust OSC contracts by considering these issues.

In this study, two postprocessing techniques, namely, conventional burnishing (CB) and ultrasonic-assisted burnishing (UAB), were applied to improve the fatigue behavior of 316 L stainless steel fabricated through selective laser melting (SLM). The effects of these processes on surface roughness, porosity, microhardness and fatigue performance were experimentally investigated. The purpose of this study is to evaluate the feasibility and effectiveness of ultrasonic-assisted burnishing as a preferred post-processing technique for enhancing the fatigue performance of additively manufactured components.

All samples were subjected to a sandblasting process. Next, the samples were divided into three distinct groups. The first group (as-Built) did not undergo any additional postprocessing, apart from sandblasting. The second group was treated with CB, while the third group was treated with ultrasonic-assisted burnishing. Finally, all samples were evaluated based on their surface roughness, porosity, microhardness and fatigue performance.

The results revealed that the initial mean surface roughness (Ra) of the as-built sample was 11.438 µm. However, after undergoing CB and UAB treatments, the surface roughness decreased to 1.629 and 0.278 µm, respectively. Notably, the UAB process proved more effective in eliminating near-surface pores and improving the microhardness of the samples compared to the CB process. Furthermore, the fatigue life of the as-built sample, initially at 66,000 cycles, experienced a slight improvement after CB treatment, reaching 347,000 cycles. However, the UAB process significantly enhanced the fatigue life of the samples, extending it to 620,000 cycles.

After reviewing the literature, it can be concluded that UAB will exceed the capabilities of CB in terms of enhancing the surface roughness and, subsequently, the fatigue performance of additive manufactured (AM) metals. However, the actual impact of the UAB process on the fatigue life of AM products has not yet been thoroughly researched. Therefore, in this study, this paper used the burnishing process to enhance the fatigue life of 316 L stainless steel produced through the SLM process.