Deployment Of Devices Research Articles

Advancing athlete safety through real-time ECG monitoring for enhanced cardiovascular health in sports performance

This research paper explores the implementation and efficacy of real-time electrocardiogram (ECG) monitoring systems for athletes, emphasizing their potential to significantly enhance safety and performance in sports settings. By utilizing advanced ECG technology, the study investigates how continuous, real-time heart rate and rhythm monitoring can aid in the immediate detection of cardiovascular anomalies during high-intensity activities. The research methodology incorporates the deployment of portable ECG devices in a controlled experimental setup, analyzing data from athletes during training sessions and competitive events. Results from the study highlight the system's ability to provide swift and accurate cardiac assessments, thereby enabling timely medical interventions. Moreover, the paper discusses the technical challenges associated with real-time ECG monitoring, such as signal interference and data accuracy, and addresses privacy and ethical considerations concerning the continuous collection of health data. The discussion extends to the implications of integrating such technology within sports medicine, suggesting that while the systems offer substantial benefits in monitoring and preventing cardiac issues, they also necessitate rigorous standards for data security and ethical oversight. The conclusion advocates for a balanced approach to the adoption of these technologies, proposing future research directions that focus on enhancing system reliability and integrating artificial intelligence to predict potential health risks proactively. This study contributes to the ongoing discourse in sports health technology by providing a comprehensive analysis of real-time ECG monitoring as a transformative tool for athlete healthcare management.

Evaluation of efficacy and safety of simplified procedure for the percutaneous closure of patent foramen ovale: the Ile De France Foramen Ovale Permeable(IDFFOP) prospective, multicenter cohort study

Abstract The superiority of device closure over medical therapy for the prevention of recurrent stroke in patients with cryptogenic stroke and patent foramen ovale (PFO) was reported in several meta –analyses of randomized controlled trials. The growing number of PFO procedures requires to find alternatives to conventional guidance by transoesophageal echocardiography (TOE) needing general anesthesia. The aim of our study was to assess prospectively the efficacy and safety of a simplified procedure based on trans thoracic echocardiography (TTE) and local anesthesia rather than TOE during PFO closure. Methods Design and study oversight. The IDFFOP study was a French prospective, multicenter cohort study performed in four centers expert in TTE-guided device deployment. All patients had a stroke history, a PFO and a large or moderate right to left shunt defined by the number of microbubbles in the left atrium ( large shunt > 30, moderate : 10 to 30 microbubbles). A(Risk of Paradoxical Embolism) score>5 was required to confirm PFO imputability in stroke history. The primary end point was efficacy of TTE during the transcatheter PFO closure defined as the successful implantation of the device without malposition, without need of TOE, and absence of significant residual shunt at 3-month TTE control. Safety was defined as the absence of any periprocedural complications. Results 280 patients were enrolled from January 2018 to October 2020 ( male sex : 59.5%; Average age 57 years old ). Average ROPE score was 7; Cumulative immediate procedural success with the TTE-guided method was 99%, without any device embolization. However, in 2 patients, TOE was needed to guide device deployment. There was no severe vascular complication, or any ischaemic event during or after the procedure. 6 post-procedure groin hematomas occurred but none needed anyblood transfusion .One patient experienced a transient periprocedural atrial fibrillation. At 3 months there were only 8 (2,8%) residual right to left shunts (3 large, 5 moderate). Conclusion A simplified procedure for percutaneous PFO closure based on TTE and local anesthesia is safe and effective. Randomized controlled trials should be performed to confirm these results.

Deployment of in-body wearable devices in healthcare and at work: closing the EU regulatory gap

Abstract Background In-body wearable devices can protect workers’ health and safety. Moreover, they might provide data that could be capitalized on in the context of the European Health Data Space (EHDS), which aims at fostering a single market for medical devices and high-risk AI systems. Yet, neither EU legislation nor national law regulates the use of in-body wearables at the workplace and the related data protection and safety issues. This regulatory gap impacts on workers’ rights. Methods The study explores whether the EU regulatory framework used in the healthcare sector for medical devices could fit the in-body wearables’ application in employment. The legal and policy analysis takes the perspective of workers’ rights and discusses the application of a key GDPR principle: lawfulness. The research discusses the potential transformations in the EU health data protection framework that could close the regulatory gap. Results The EU regulatory framework for medical devices (focused on product safety, performance, and quality) is not equipped to address the data protection challenges linked to the use of in-body wearables for occupational health and safety purposes. The GDPR’s principle of lawfulness and the related guidance by EU and national data protection authorities lack clarity on employers’ possibilities to apply in-body wearables for occupational health purposes. Filling the regulatory gap is conducive to appropriate qualification and risk-based classification of in-body wearables, which affect pre- and post-market requirements and impact on data protection. Conclusions a. The EU legislator could use its social policy competence to regulate the use of in-body wearables to protect workers’ health while safeguarding their data. b. EU authorities should clarify whether and how the data collected by in-body wearables at the workplace could be shared and (re)used within the EHDS. c. Further input by data protection authorities is needed to close the regulatory gap. Key messages • The EU should regulate the use of in-body wearables to protect workers’ health and safety. • Health data gathered through the application of in-body wearables at the workplace may be an asset for the EHDS.

Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification

Abstract. Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deep learning technologies, indoor visual positioning based on image recognition has become increasingly mature. This method offers the benefits of low cost and does not require additional hardware installation. However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. The system is divided into two stages: preprocessing and real-time positioning. In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. In the real-time positioning stage, the system first uses mobile devices such as smartphones to obtain real-time video streams. A key frame recognizer based on convolutional neural networks identifies key frames in each video stream frame, thereby obtaining approximate positions for rough positioning. Second, feature points are identified in each frame of the video stream and matched with feature points with location information in the built environmental feature information database to calculate precise positions for fine positioning. It has significant optimizations compared with conventional visual solutions in terms of preprocessing data collection, algorithm performance consumption, and versatility.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

ETLSH-YOLO: An Edge–Real-Time Transmission Line Safety Hazard Detection Method

Using deep learning methods to detect potential safety hazards in transmission lines is the mainstream method for power grid security monitoring. However, the existing model is too complex to adapt to edge device deployment and real-time detection. Therefore, an edge–real-time transmission line safety hazard detection method (ETLSH-YOLO) was proposed to reduce the model’s complexity and improve the model’s robustness. Firstly, a re-parameterized Ghost efficient layer aggregation network (RepGhostCSPELAN) was designed to effectively fuse the feature information of different layers while enhancing the model’s expression ability and reducing the number of model parameters and floating-point operations. Then, a spatial channel decoupled downsampling block (CSDovn) was designed to reduce computational redundancy and improve the computational efficiency of the model. Then, coordinate attention (CA) was added in the process of multi-scale feature fusion to suppress the interference of complex background and improve the global perception ability of the model object. Finally, the Mish activation function was used to improve the network’s training speed, convergence, and generalization ability. The experimental results show that the mAP50 of this model improved by 1.73% compared with the baseline model, and the number of parameters and floating-point operations were reduced by 33.96% and 22.22%, respectively. This model lays the foundation for solving the dilemma of edge device deployment.

Design of thermally programmable 3D shape memory polymer-based devices tailored for endovascular treatment of intracranial aneurysms

Despite recent technological advancements in endovascular embolization devices for treating intracranial aneurysms (ICAs), incomplete occlusion and aneurysm recanalization remain critical challenges. Shape memory polymer (SMP)-based devices, which can be manufactured and tailored to patient-specific aneurysm geometries, possess the potential to overcome the suboptimal treatment outcome of the gold standard: endovascular coiling. In this work, we propose a highly porous patient-specific SMP embolic device fabricated via 3D printing to optimize aneurysm occlusion, and thus, improve the long-term efficacy of endovascular treatment. To facilitate device deployment at the aneurysm via Joule-heating, we introduce a stable, homogeneous coating of poly-pyrrole (PPy) to enhance the electrical conductivity in the SMP material. Using an in-house pulse width modulation circuit, we induced Joule-heating and characterized the shape recovery of the PPy-coated SMP embolic devices. We found that the employed PPy coating enables enhanced electrical and thermal conductivity while only slightly altering the glass transition temperature of the SMP material. Additionally, from a series of parametric studies, we identified the combination of catalyst concentration and pyrrole polymerization time that yielded the shape recovery properties ideal for ICA endovascular therapy. Collectively, these findings highlight a promising material coating for a future coil-free, personalized shape memory polymer (SMP) embolic device, designed to achieve long-lasting, complete occlusion of aneurysms.

Thin film synthesis and photovoltaic performance of polypyrrole@cobalt hydroxide composites for solar cells and optoelectronic devices

Mono-nanocomposites (MNCs) comprising polypyrrole (PPy) and cobalt hydroxide nanoparticles (Co(OH)2 NPs) are emerging as highly favorable contenders for applications in solar cells and optoelectronic devices, owing to their distinctive attributes encompassing robust light absorption, elevated conductivity, and commendable chemical stability. This investigation delves into the production of a thin film [PPy@Co(OH)2]MNC utilizing the physical vapor deposition (PVD) method. The structural and morphological characteristics of the [PPy@Co(OH)2]MNC thin film were scrutinized through X-ray diffraction (XRD) and scanning electron microscopy (SEM). Optical properties, namely reflectance (R), absorbance (Abs), and transmittance (T) within the UV–vis–NIR spectrum, were precisely gauged at room temperature. Time-dependent density functional theory (TD-DFT) calculations and optimizations were conducted to analyze the geometric parameters, while the refractive index dispersion was probed using the single oscillator Wemple-Didomenico (WD) model. Additionally, the energy of a single oscillator and the energy of dispersion were quantified. The [PPy@Co(OH)2]MNC thin film exhibited pronounced light absorption across the UV–vis–NIR spectrum, characterized by a bandgap of 1.6 eV. The Au/[PPy@Co(OH)2]MNC/p-Si/Al heterojunction device demonstrated a power conversion efficiency and fill factor (FF) of 2.6 % and 55.07 %, respectively, under an irradiance of 100 W/cm2. The findings of this investigation underscore the potential of [PPy@Co(OH)2]MNC-based thin films as auspicious candidates for deployment in solar cells and optoelectronic devices.

Enhanced pediatric thyroid ultrasound image segmentation using DC-Contrast U-Net

Early screening methods for the thyroid gland include palpation and imaging. Although palpation is relatively simple, its effectiveness in detecting early clinical signs of the thyroid gland may be limited, especially in children, due to the shorter thyroid growth time. Therefore, this constitutes a crucial foundational work. However, accurately determining the location and size of the thyroid gland in children is a challenging task. Accuracy depends on the experience of the ultrasound operator in current clinical practice, leading to subjective results. Even among experts, there is poor agreement on thyroid identification. In addition, the effective use of ultrasound machines also relies on the experience of the ultrasound operator in current clinical practice. In order to extract sufficient texture information from pediatric thyroid ultrasound images while reducing the computational complexity and number of parameters, this paper designs a novel U-Net-based network called DC-Contrast U-Net, which aims to achieve better segmentation performance with lower complexity in medical image segmentation. The results show that compared with other U-Net-related segmentation models, the proposed DC-Contrast U-Net model achieves higher segmentation accuracy while improving the inference speed, making it a promising candidate for deployment in medical edge devices in clinical applications in the future.

Assessing post-TAVR cardiac conduction abnormalities risk using an electromechanically coupled beating heart.

Transcatheter aortic valve replacement (TAVR) has rapidly displaced surgical aortic valve replacement (SAVR). However, certain post-TAVR complications persist, with cardiac conduction abnormalities (CCA) being one of the major ones. The elevated pressure exerted by the TAVR stent onto the conduction fibers situated between the aortic annulus and the His bundle, in proximity to the atrioventricular (AV) node, may disrupt the cardiac conduction leading to the emergence of CCA. In this study, an in silico framework was developed to assess the CCA risk, incorporating the effect of a dynamic beating heart and preprocedural parameters such as implantation depth and preexisting cardiac asynchrony in the new onset of post-TAVR CCA. A self-expandable TAVR device deployment was simulated inside an electromechanically coupled beating heart model in five patient scenarios, including three implantation depths and two preexisting cardiac asynchronies: (i) a right bundle branch block (RBBB) and (ii) a left bundle branch block (LBBB). Subsequently, several biomechanical parameters were analyzed to assess the post-TAVR CCA risk. The results manifested a lower cumulative contact pressure on the conduction fibers following TAVR for aortic deployment (0.018MPa) compared to nominal condition (0.29MPa) and ventricular deployment (0.52MPa). Notably, the preexisting RBBB demonstrated a higher cumulative contact pressure (0.34MPa) compared to the nominal condition and preexisting LBBB (0.25MPa). Deeper implantation and preexisting RBBB cause higher stresses and contact pressure on the conduction fibers leading to an increased risk of post-TAVR CCA. Conversely, implantation above the MS landmark and preexisting LBBB reduces the risk.

Comparative analysis of online voltage stability indices based on synchronized PMU measurements

The need for reliable real-time information on voltage stability margins of electrical power systems is an increasingly relevant concern within the current trend of electrification and deployment of power electronics-based devices. This paper conducts the assessment and comparison of four Voltage Stability Indices (VSIs) proposed for this application and based exclusively on synchronized phasor measurements. The robustness and accuracy of each method in identifying the point of maximum power transfer are evaluated as the correlation between load characteristics and consistent estimation of voltage stability margins is explored. In addition, the likelihood inherent to each VSI formulation of triggering false alarms under certain system dynamics is addressed in detail. The comparative analyses are derived from dynamic simulation data of a 3-bus test system, the IEEE 9-bus network and the IEEE 39-bus network, all modelled in the open-source Python-based power system simulator DynPSSimPy. Case studies cover placement of monitoring device, different load types, line disconnection events and presence of measurement noise. The results presented serve as a reference point for the development and/or enhancement of VSIs suitable for real-time applications, highlighting their most significant advantages and drawbacks and providing insights on potential trade-offs that need to be considered when employing such approaches within control center settings.

Acute lower limb ischemia caused by repeat deployment of vascular closure device.

Vascular closure devices are used to facilitate faster hemostasis and earlier ambulation, improve patient comfort, and reduce length of hospital stay after percutaneous endovascular procedures. However, their use may rarely be associated with limb ischemia due to endothelial damage and arterial thrombosis. This report illustrates the case of a patient who experienced acute lower limb ischemia due to superficial femoral artery occlusion caused by repeat closure with Angio-Seal vascular closure device within 30 days in a small-caliber superficial femoral artery. The patient was surgically treated by vascular repair with a synthetic graft, and remains symptom-free in 3-year follow-up. Repeat deployment of vascular closure devices in the same access site within a period of 30 days may cause acute limb ischemia due to arterial thrombosis, especially in patients with small-caliber arteries, even in the absence of evident risk factors.

Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis.

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection

Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices.

The influence of revised ordinance on radiation protection management in Japanese hospitals: device deployment and involvement of radiology technologists.

This study evaluates the impact of the 2021 revision of Japan's Ordinance on the Prevention of Ionizing Radiation Hazards on radiation protection practices, focusing on the deployment of radiation protection devices and the involvement of radiology technologists in Japanese hospitals. A two-phase web-based questionnaire survey was conducted among hospitals registered as training facilities with the Japanese Radiological Society. The survey included 53 questions covering facility information, radiation worker management, training, and working environment. The use of lens-specific dosimeters significantly increased post-revision (p = 0.005). Protective eyewear availability showed minor improvements, particularly in angiographic rooms (p = 0.019). The involvement of radiology technologists remained high in angiographic rooms but showed no significant changes in endoscopy and fluoroscopy rooms. Larger hospitals exhibited better compliance with protective measures, though gaps in resource allocation persisted. The ordinance revision led to significant improvements in dosimeter usage but only minor changes in protective eyewear deployment and technologist involvement.

Improving IIoT security: Unveiling threats through advanced side-channel analysis

The widespread deployment of IIoT edge devices makes them attractive victims for malicious activities. Consequently, how to implement trustworthy operations becomes a realistic topic in embedded systems. While most current physical systems for detecting malicious activities primarily focus on identifying known intrusion codes at the block level, they ignore that even an unnoticeable injected function can result in system-wide loss of security. In this paper, we propose a framework called CNDSW built on deep-learning side-channel analysis for function-level industrial control flow integrity monitoring. By collaboratively utilizing correlation analysis and deep-learning techniques, the dual window sliding monitoring mechanism in the proposed CNDSW framework demonstrates a real-time code intrusion tracking capacity on embedded controllers with a 99% detection accuracy on average. Instead of focusing on known block-level intrusions, we experimentally show that our model is feasible to detect function-level code intrusions without knowing the potential threat type. Besides, we further explore how different configurations of the CNDSW framework can help the monitoring process with different emphases and to which extent the model can concurrently detect multiple code intrusion activities. All our experiments are conducted on 32-bit ARM Cortex-M4 and 8-bit RISC MCUs across five different control flow programs, providing a comprehensive evaluation of the framework’s capabilities.

Research on Improved Algorithms for Cone Bucket Detection in Formula Unmanned Competition.

The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly affecting detection efficiency. A lightweight detection model based on YOLOv8 is proposed to address these problems. The model includes improving the backbone network, neck network, and detection head, as well as introducing knowledge distillation and other techniques to construct a lightweight model. The specific improvements are as follows: firstly, the backbone network for extracting features is improved by introducing the ADown module in YOLOv9 to replace the convolution module used for downsampling in the YOLOv8 network, and secondly, the FasterBlock in FasterNet network was introduced to replace the fusion module in YOLOv8 C2f, and then the self-developed lightweight detection head was introduced to improve the detection performance while achieving lightweight. Finally, the detection performance was further improved by knowledge distillation. The experimental results on the public dataset FSACOCO show that the improved model's accuracy, recall, and average precision are 92.7%, 84.6%, and 91%, respectively. Compared with the original YOLOv8n detection model, the recall and average precision increase by 2.7 and 1.2 percentage points, the memory is half the original, and the model computation is 51%. The model significantly reduces the misdetection and leakage of conical buckets in real-vehicle tests and, at the same time, ensures the detection speed to satisfy the deployment requirements on tiny devices. Satisfies all the requirements for deployment of tiny devices in the race car of the China University Student Driverless Formula Competition. The improved method in this paper can be applied to conebucket detection in complex scenarios, and the improved idea can be carried over to the detection of other small targets.

Enhanced Corrosion Protection of Printed Circuit Board Electronics using Cold Atmospheric Plasma-Assisted SiOx Coatings.

The miniaturization and widespread deployment of electronic devices across diverse environments have heightened their vulnerability to corrosion, particularly affecting copper traces within printed circuit boards (PCBs). Conventional protective methods, such as conformal coatings, face challenges including the necessity for a critical thickness to ensure effective barrier properties and the requirement for multiple steps of drying and curing to eliminate solvent entrapment within polymer coatings. This study investigates cold atmospheric plasma (CAP) as an innovative technique for directly depositing ultrathin silicon oxide (SiOx) coatings onto copper surfaces to enhance corrosion protection in PCBs. A systematic investigation was undertaken to examine how the scanning speed of the CAP deposition head impacts the film quality and corrosion resistance. The research aims to determine the optimal scanning speed of the CAP deposition head that achieves complete surface coverage while promoting effective cross-linking and minimizing unreacted precursor entrapment, resulting in superior electrical barrier and mechanical properties. The CAP coating process demonstrated the capability of depositing SiOx onto copper surfaces at various thicknesses ranging from 70 to 1110 nm through a single deposition process by simply adjusting the scanning speed of the plasma head (5-75 mm/s). Evaluation of material corrosion barrier characteristics revealed that scanning speeds of 45 mm/s of the plasma deposition head provided an effective coating thickness of 140 nm, exhibiting superior corrosion resistance (30-fold) compared to that of uncoated copper. As a proof of concept, the efficacy of CAP-deposited SiOx coatings was demonstrated by protecting an LED circuit in saltwater and by coating printed circuits for potential agricultural sensor applications. These CAP-deposited coatings offer performance comparable to or superior to traditional conformal polymeric coatings. This research presents CAP-deposited SiOx coatings as a promising approach for effective and scalable corrosion protection in miniaturized electronics.

Comprehensive numerical prototyping of paper-based microfluidic devices using open-source tools

Paper-based microfluidics has emerged as a promising field with diverse applications ranging from medical diagnostics to environmental monitoring. Despite significant progress in research and development, the translation of paper-based prototypes into practical end-user devices remains limited. This limitation stems from challenges related to devices not being sufficiently portable and autonomous, which paper-based microfluidics is expected to overcome. Yet for this purpose, we note the lack of comprehensive numerical modeling tools capable of simulating the intricate physicochemical phenomena involved in order to optimize the development process; hence, in this study, we introduce porousMicroTransport, a novel simulation package integrated with the open-source platform OpenFOAM®, designed to address these challenges. porousMicroTransport offers efficient solvers for fluid flow and transport phenomena in microfluidic porous media, including capillarity models and (bio)chemical reactions. Moreover, under horizontal flow conditions, porousMicroTransport application field can be extended to any porous media. We demonstrate the software’s effectiveness in two example cases, showcasing its ability to accurately reproduce complex phenomena involved in paper-based devices. By virtue of being an easy-to-use and computationally efficient tool, porousMicroTransport facilitates the design and optimization of devices, potentially enabling more devices to meet the WHO’s REASSURED criteria for point-of-care testing. We anticipate that this tool will accelerate the development and deployment of robust and portable diagnostic devices, bridging the gap between research and practical applications.

Advanced Setup and Techniques for Endovascular Treatment of Ruptured Intracranial Aneurysms

Despite advancements in neurosurgical techniques, subarachnoid hemorrhage(SAH) caused by the rupture of a partially thrombosed intracranial giant aneurysm remains a challenging clinical entity. This report describes the successful treatment of an 80-year-old male patient with SAH due to a ruptured, partially thrombosed intracranial giant aneurysm. The patient underwent a staged endovascular strategy using a flow diverter. The patient presented with SAH secondary to a ruptured, partially thrombosed intracranial giant aneurysm located at the C2 portion of the internal carotid artery and involving the origin of the posterior communicating artery(Pcom). Imaging revealed a dorsomedial rupture point on the aneurysm. A two-stage endovascular intervention(IVR) was performed. The first stage involved coil embolization aimed at covering the rupture point. Following the resolution of the vasospasm and the acute phase of SAH, the second stage involved the deployment of a pipeline embolization device. Digital subtraction angiography performed one month after the second stage IVR demonstrated a significant reduction in aneurysm filling, with preserved flow to the Pcom artery. We will discuss the technical details and rationale behind the staged endovascular approach in this complex case.