Search results

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.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

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.

The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

Based on the cognition–affect–conation pattern, this study explores the factors that affect the intention to use facial recognition services (FRS). The study adopts the driving factor perspective to examine how network externalities influence FRS use intention through the mediating role of satisfaction and the barrier factor perspective to analyze how perceived privacy risk affects FRS use intention through the mediating role of privacy cynicism.

The data collected from 478 Chinese FRS users are analyzed via partial least squares-based structural equation modeling (PLS-SEM).

The study produces the following results. (1) FRS use intention is motivated directly by the positive affective factor of satisfaction and the negative affective factor of privacy cynicism. (2) Satisfaction is affected by cognitive factors related to network externalities. Perceived complementarity and perceived compatibility, two indirect network externalities, positively affect satisfaction, whereas perceived critical mass, a direct network externality, does not significantly affect satisfaction. In addition, perceived privacy risk generates privacy cynicism. (3) Resistance to change positively moderates the relationship between privacy cynicism and intention to use FRS.

This study extends knowledge on people's use of FRS by exploring affect- and cognitive-based factors and finding that the affect-based factors (satisfaction and privacy cynicism) play fully mediating roles in the relationship between the cognitive-based factors and use intention. This study also expands the cognitive boundaries of FRS use by exploring the functional condition between affect-based factors and use intention, that is, the moderating role of resistance to use.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

This study aims to investigate the thermal fracture mechanism of moisture-preconditioned SAC305 ball grid array (BGA) solder joints subjected to multiple reflow and thermal cycling.

The BGA package samples are subjected to JEDEC Level 1 accelerated moisture treatment (85 °C/85%RH/168 h) with five times reflow at 270 °C. This is followed by multiple thermal cycling from 0 °C to 100 °C for 40 min per cycle, per IPC-7351B standards. For fracture investigation, the cross-sections of the samples are examined and analysed using the dye-and-pry technique and backscattered scanning electron microscopy. The packages' microstructures are characterized using an energy-dispersive X-ray spectroscopy approach. Also, the package assembly is investigated using the Darveaux numerical simulation method.

The study found that critical strain density is exhibited at the component pad/solder interface of the solder joint located at the most distant point from the axes of symmetry of the package assembly. The fracture mechanism is a crack fracture formed at the solder's exterior edges and grows across the joint's transverse section. It was established that Au content in the formed intermetallic compound greatly impacts fracture growth in the solder joint interface, with a composition above 5 Wt.% Au regarded as an unsafe level for reliability. The elongation of the crack is aided by the brittle nature of the Au-Sn interface through which the crack propagates. It is inferred that refining the solder matrix elemental compound can strengthen and improve the reliability of solder joints.

Inspection lead time and additional manufacturing expenses spent on investigating reliability issues in BGA solder joints can be reduced using the study's findings on understanding the solder joint fracture mechanism.

Limited studies exist on the thermal fracture mechanism of moisture-preconditioned BGA solder joints exposed to both multiple reflow and thermal cycling. This study applied both numerical and experimental techniques to examine the reliability issue.

This paper aims to answer these questions: “Is the public adopting energy-saving and water-saving facilities because they want to save energy and water in their psychological perception?”, “Is it convenient to use energy-saving and water-saving facilities?”, “If the inductive design of energy-saving and water-saving facilities attracts the public’s interest, the public is it more willing to install energy-saving and water-saving facilities in a widespread manner?” and “Can inductive energy-saving and water-saving facilities be introduced into the smart manufacturing system of manufacturing industries that require a lot of water to effectively save water and save costs for the company?”.

This paper aims to investigate the attitudes of employees toward using energy-saving and water-saving facilities by constructing a questionnaire based on the ABC (Affect, Behavior, Cognition) model to survey the attitudes of employees from the Southern and Eastern of Taiwan and establishing a structural equation modeling (SEM) to examine the relationship between affect, behavior and cognition while using energy-saving and water-saving facilities.

There are some findings in this paper that the affective design have a strongly significant positive impact for using energy-saving and water-saving facility in the proposed model. People are willing to use energy-saving and water-saving facilities but are more willing to adopt those energy-saving and water-saving products of smart designs, as those take into account the emotional factors. The critical factor for the public to adopt energy-saving and water-saving facilities is smart design, which incorporates emotional elements.

There are still some limitations of this study that the ABC model can only be used as a psychological discussion, and the development and design of related facilities still needs to be jointly developed with professionals in related technical fields. The introduction of induction water supply facilities needs to be considered while the company introduces the design of the smart manufacturing system. Therefore, professionals related to induction water supply should participate in the planning at the initial stage of the company's concept of introducing the smart manufacturing system.

On the practical side, based on preliminary research conclusions, this study proposes to introduce inductive water supply into smart manufacturing systems for manufacturing companies that require a lot of water in their manufacturing processes. In practice, the company can actually save a lot of water, thereby saving costs and reducing waste water discharge.

The results of this study show that the public has a cognition of energy-saving and water-saving. However, there is a Chinese proverb that “easy to know and hard to do”, when actually using facilities, convenience is an important consideration for public. Smart facilities of energy-saving and water-saving, in addition to the benefits of energy-saving and water-saving, it is easy to use, and interacts with users through inductive water supply, which can more emotionally attract people's willingness to use.

This study found that smart facilities, which can more emotionally attract people's willingness to use. On the academic side, this study proves that using the ABC theory to explore the public’s psychological affective, behavior and cognition response to the use of facilities is a very suitable method. On the practical side, based on preliminary research conclusions, this study proposes to introduce inductive water supply into smart manufacturing systems for manufacturing companies that require a lot of water in their manufacturing processes. In practice, the company can actually save a lot of water, thereby saving costs and reducing waste water discharge.

This study aims to investigate the possible defects and their root causes in a soft-termination multilayered ceramic capacitor (MLCC) when subjected to a thermal reflow process.

Specimens of the capacitor assembly were subjected to JEDEC level 1 preconditioning (85 °C/85%RH/168 h) with 5× reflow at 270°C peak temperature. Then, they were inspected using a 2 µm scanning electron microscope to investigate the evidence of defects. The reliability test was also numerically simulated and analyzed using the extended finite element method implemented in ABAQUS.

Excellent agreements were observed between the SEM inspections and the simulation results. The findings showed evidence of discontinuities along the Cu and the Cu-epoxy layers and interfacial delamination crack at the Cu/Cu-epoxy interface. The possible root causes are thermal mismatch between the Cu and Cu-epoxy layers, moisture contamination and weak Cu/Cu-epoxy interface. The maximum crack length observed in the experimentally reflowed capacitor was measured as 75 µm, a 2.59% difference compared to the numerical prediction of 77.2 µm.

This work's contribution is expected to reduce the additional manufacturing cost and lead time in investigating reliability issues in MLCCs.

Despite the significant number of works on the reliability assessment of surface mount capacitors, work on crack growth in soft-termination MLCC is limited. Also, the combined experimental and numerical investigation of reflow-induced reliability issues in soft-termination MLCC is limited. These cited gaps are the novelties of this study.

This study aims to investigate the effects of nano or inorganic fillers on unsaturated polyester’s (UPE) thermal, mechanical, and physical properties. UPE reinforced with nanoparticles shows better properties than the pure polymer itself. Nano or inorganic fillers are used in the polymeric matrix to improve thermal, mechanical and physical properties.

To improve thermal, mechanical and physical properties, UPE resin was modified with silica (S), boron nitride (BN) and S/BN hybrid nanoparticles at different ratios. Viscosity and solids content measurement, Fourier transform infrared spectroscopy, contact angle measurement, scanning electron microscopy (SEM), thermogravimetric analysis (TGA) and thermal conductivity coefficient tests were performed on the samples.

In the SEM analysis, the UPE sample showed a smooth appearance, while all samples containing additives showed phase separation and overall heterogeneous distribution. TGA results demonstrated that the thermal stability of the resin increased in the presence of S and BN additives. According to the results, it was observed that the presence of S and BN additives in the UPE resin and the use of certain ratios improved the resin properties.

As a result of the literature search, to the best of the authors’ knowledge, no study was found in which BN nanoparticles were included in the UPE resin together with S.