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Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.

The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.

On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.

The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.

The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.

This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

Left atrial appendage occlusion (LAAO) is a structural interventional cardiology procedure that offers several possibilities for the application of additive manufacturing technologies. The literature shows a growing interest in the use of 3D-printed models for LAAO procedure planning and occlusion device choice. This study aims to describe a full workflow to create a 3D-printed LAA model for LAAO procedure planning.

The workflow starts with the patient’s computed tomography diagnostic image selection. Segmentation in a commercial software provides initial geometrical models in standard tessellation language (STL) format that are then preprocessed for print in dedicated software. Models are printed using a commercial stereolithography machine and postprocessing is performed.

Models produced with the described workflow have been used at the Careggi Hospital of Florence as LAAO auxiliary planning tool in 10 cases of interest, demonstrating a good correlation with state-of-the-art software for device selection and improving the surgeon’s understanding of patient anatomy and device positioning.

3D-printed models for the LAAO planning are already described in the literature. The novelty of the article lies in the detailed description of a robust workflow for the creation of these models. The robustness of the method is demonstrated by the coherent results obtained for the 10 different cases studied.

Various scholars suggest that there is a lack of research on the recruitment in small and medium-sized enterprises (SMEs) and also a scarcity of theoretical basis for the recruitment procedures used by these companies. As the vast majority of studies concentrate on larger organizations, they may not accurately reflect the challenges faced by smaller-sized entities to profoundly and accurately comprehend their recruitment procedures. In addition, the use of technology in recruitment has grown in importance in today’s quickly evolving business environment, particularly in light of the COVID-19 pandemic footprint. This study aims to examine the recruitment procedures used by SMEs and how they have been compelled to adjust to different extents to these technological improvements by the effects of the aforementioned epidemic.

With the aim to investigate the current recruitment practices in SMEs and the extent to which digital technologies are embraced by these companies within human resources (HR) procedures, this research relied on interviews with SMEs representatives. The qualitative methods used provided access to relevant data and insights, as they allowed close interactions with top managers and CEOs of ten companies from various sectors. Thus, the research results draw a vivid and reliable image of the procedures and practices used by small and medium-sized companies to attract, select and retain their staff.

This study’s findings are of increased interest to HR professionals, recruiters and managers in SMEs, who aim to attract and retain the best talent and optimize their recruitment strategies in a rapidly changing business environment, enabled by technological advancements. Effective HR recruitment procedures adapted to the specific needs of small and medium-sized companies can lead to several benefits for the organization, including improved employee selection, reduced turnover and increased organizational productivity.

Although the interviews examined here encompass recruitment techniques from SMEs in a variety of industries, the results’ generalizability is limited by the sample size and geography. Furthermore, the findings’ dependability is dependent on the accuracy of the data provided by the respondents.

This investigation confirms some of the theoretical underpinnings which point to the lack of formalized structures and procedures in the recruitment process in SMEs, which enjoy more flexibility in managing HR processes. In addition, the results reinforce the arguments indicating an adjustment between HR strategies or policies and organizational goals in smaller enterprises which adapt faster to changes in the market. Moreover, it becomes apparent that there is a relationship between the quality of job descriptions and the successful fit in attracting the right candidates for the open positions. Furthermore, digital technologies offer opportunities for expanding the recruiters’ reach to a wider audience and also support the selection stage, thus increasing the chances of finding suitable staff. As the need to shift from traditional recruitment to e-recruitment in SMEs has been highlighted in the literature, the qualitative research revealed that this need was driven on the one hand by the COVID-19 pandemic when these companies successfully adapted and implemented new online methods of recruiting, but also by the lack of skilled labor, leading to the expansion of recruitment to other parts of the country or even to other countries.

With regard to the proportion of men and women used in small and medium-sized companies, there is a clear need to involve and train more women in the predominantly male-dominated industrial and IT sectors. From this point of view, companies tend to devote more interest to integrating communities of women in these industries, as well as in key management positions. Another point of interest that the study highlights is the fact that SMEs have started to get creative with the benefits package they propose to candidates and focus on remote work, hybrid office–home working, or seasonal work to offer future employees a better work–life balance.

The added value of this investigation is filling the gaps in the current literature concerning recruitment procedures currently used by SMEs, the challenges they face and the solutions they advanced to solve them. Furthermore, SMEs often drive innovation and competition in the market and play a crucial role in the supply chain of larger companies, providing them with the goods and services they need to operate and supporting the availability and reliability of products from larger companies. They are often the driving force behind revitalizing local economies and creating new employment opportunities. Consequently, the underlying significance of this study is rooted in the need to modernize and simultaneously improve HR recruitment procedures through the integration of technology and a focus on innovation.

The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.

The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator.

The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B₀ field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart.

The MRI compatible actuator uses the B₀ field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.

This study aims to predict the effect of cultural distance, perceived risk and electronic word of mouth (eWOM) on higher education institutes' students' destination image. In addition, it examines the mediating role of destination image in relation to students' travel intentions.

An online survey was employed to collect data from 200 graduate and postgraduate students. The partial least squares was employed to analyse the hypothesised relationships.

The results of this study found support for the positive effect of cultural distance and eWOM on destination image. Additionally, the mediating effect of destination image was also supported.

This research confirms the vital role of destination image as an antecedent of students' future intention to visit the destination. Moreover, this study contributes to marketing theory by predicting the critical drivers of higher education students' destination image and discussing their applications in the education sector.

The purpose of the study is to compare methodologies for mapping a historic building, with image capture by smartphones and drones, using photogrammetric techniques for three-dimensional (3D) modeling of the structure. Processes and products are also analyzed, as well as possibilities for storing and visualizing data for structuring a cadastre of historical and artistic heritage are studied.

For mapping with smartphones, the overlapping of photographs was guaranteed, with data acquisition using three different cameras, on the same date as the aerial survey. The models were made from different combinations of camera use. For storage, a conceptual model based on ISO 19.152:2012 is proposed, which was implemented in the MongoDB, resulting in a database for storage. The visualization was carried out on the Cesium ion platform.

The results indicate that the terrestrial 3D reconstruction using smartphones is an efficient alternative to the historical and artistic cadastre, presenting texture quality superior to the aerial survey in a shorter production time. When dealing with the conceptual model, the LADM (Land Administration Domain Model) standardization guarantees interoperability and facilitates data exchange. In addition, it proved to be flexible for the creation of thematic profiles, supporting their effective storage. The insertion of data in the visualization platform was simple and effective, and it even generated sharing links for visualization of the models.

The study analyses a low-cost method with the use of easily accessible devices, with a combination of methodologies and applied techniques. The data storage and visualization method is also simple and flexible, suitable for application in the cadastre of historical heritage.

We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.

Porosity is one of the most important properties of the textile substrate. It can influence the comfort of a garment by affecting its breathability and thermal conductivity. During the process of dyeing, the dye liquor comes in contact with the substrate; the absorption of the dye liquor into the substrate will be dependent on its porosity. The concept of porosity between the yarns of fabric is a common phenomenon; however, the porosity between the fibres in the yarn can also influence the dyeing behaviour of the fabric.

In this research, ring and rotor yarns of 25/s and 30/s counts are considered as textile substrates. The porosity of yarns was determined theoretically and experimentally using the image analysis method.

It was found that theoretical porosity is independent of the yarn manufacturing method. In addition, 30/s yarn was more porous as compared with 25/s yarn having a higher pore area. Rotor yarns had higher porosity, dye fixation and K/S as compared with ring yarns. Dyeing behaviour was also dependent on the count of yarn. Specifically, 30/s yarns have higher dye fixation as compared with 25/s yarns. However, 25/s yarns were dyed with deeper shades showing higher K/S values. Also, 25/s yarns are coarser than 30/s yarns having higher diameters and cross-sectional area, thus resulting in deeper shades and higher K/S values.

This novel technique is based on the comparative study of the porosity of various types of yarns using the image analysis technique. This investigation shows that the porosity between the fibres in the yarn can also influence the dyeing behaviour of the yarn.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.