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Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

This study aims to investigate the role of interpersonal influence on consumer purchase decisions regarding foreign products, specifically by exploring consumers’ social reaction styles (acquisitive and protective) when confronted with normative pressures and their subsequent impact on consumers’ purchase behavior in the context of situational animosity.

Three studies were conducted in China to empirically examine the proposed research model. The US–China Chip War of 2022 was used as the research context for situational animosity, while the Japan–China relationship representing a stable animosity condition was used for contrast.

This study establishes the mediating role of perceived normative pressure in linking animosity attitudes to purchase avoidance in situational animosity. It also validates that consumers’ social reaction styles (acquisitive and protective) help predict distinct behavioral outcomes, holding significant implications for advancing research in the field of product and brand consumption.

This research provides a novel perspective by exploring consumers’ social reaction styles when dealing with normative pressure in situational animosity. The distinction between acquisitive and protective reaction styles adds depth and originality to the study. Moreover, this study examines consumer behavior in two distinct consumption contexts: switching intentions to local products and purchase intentions for products from offending countries in hidden consumption situations. This dual perspective offers a comprehensive exploration of consumers’ purchase behavior under normative pressure, contributing to the novelty of this research.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

The purpose of this paper is to report on the results of a study carried out to identify and analyse which potential subject areas may have impact on developments in the field of building maintenance (BM). That is, it is intended to contribute to the integration of new approaches so that building maintenance management (BMM) becomes as automated, digital and intelligent or smartness as possible in the near future.

The research approach has resulted in a theory that is essentially based on a qualitative design. The route followed was a literature review, involving the collection, analysis and interpretation of carefully selected information, mostly from recently published records. The data assembled and the empirical experience itself made it possible to present a comprehensive viewpoint and some future outlooks.

Five thematic areas considered as potentially impactful for BM developments have been highlighted, analysed and generically labelled as thematic base words, which are monitoring, automation, digitalisation, intelligence and smart. It is believed that these may be aspects that will lay the groundwork for a much more advanced and integrated agenda, featured by a high-tech vision.

This is thought to be a different way of looking at the problem, as it addresses five current issues together. Trendy technological aspects are quite innovative and advantageous for BMM, providing opportunities not yet widely explored and boosting the paradigm shift.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

This paper aims to present a literature review of remote monitoring systems for water infrastructure in the Global South.

Following initial scoping searches, further examination was made of key remote monitoring technologies for water infrastructure in the Global South. A standard literature search methodology was adopted to examine these monitoring technologies and their respective deployments. This hierarchical approach prioritised “peer-reviewed” articles, followed by “scholarly” publications, then “credible” information sources and, finally, “other” relevant materials. The first two search phases were conducted using academic search services (e.g. Scopus and Google Scholar). In the third and fourth phases, Web searches were carried out on various stakeholders, including manufacturers, governmental agencies and non-governmental organisations/charities associated with Water, Sanitation and Hygiene (WASH) in the Global South.

This exercise expands the number of monitoring technologies considered in comparison to earlier review publications. Similarly, preceding reviews have largely focused upon monitoring applications in sub-Saharan Africa (SSA). This paper explores opportunities in other geographical regions and highlights India as a significant potential market for these tools.

This review predominantly focuses upon information/data currently available in the public domain.

Remote monitoring technologies enable the rapid detection of broken water pumps. Broken water infrastructure significantly impacts many vulnerable communities, often leading to the use of less protected water sources and increased exposure to water-related diseases. Further to these public health impacts, there are additional economic disadvantages for these user communities.

This literature review has sought to address some key technological omissions and to widen the geographical scope associated with previous investigations.

Social media (SM) platforms tempt individuals to communicate their perspectives in real-time, rousing engaging discussions on countless topics. People, besides using these platforms to put up their problems and solutions, also share activist content (AC). This study aims to understand why people participate in activist AC sharing on SM by investigating factors related to planned and unplanned human behaviour.

The study adopted a quantitative approach and administered a close-ended structured questionnaire to gather data from 431 respondents who shared AC on Facebook. The data was analysed using hierarchical regression in SPSS.

The study found a significant influence of both planned (perceived social gains (PSGs) , altruism and perceived knowledge (PK)) and unplanned (extraversion and impulsiveness) human behaviour on activist content-sharing behaviour on SM. The moderating effect of enculturation and general public opinion (GPO) was also examined.

Sharing AC on SM is not like sharing other forms of content such as holiday recommendations – the former can provoke consequences (sometimes undesirable) in some regions. Such content can easily leverage the firehose of deception, maximising the vulnerability of those involved. This work, by relating human behaviour to AC sharing on SM, offers significant insights to enable individuals to manage their shared content and waning probable consequences.

This work combined two opposite constructs of human behaviour: planned and unplanned to explain individual behaviour in a specific context of AC sharing on SM.