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Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.

AKI survivors experience gaps in care that contribute to worse outcomes, experience, and cost.Challenges to optimal care include issues with information transfer, education, collaborative care, and use of digital health tools.Research is needed to study these challenges and inform optimal use of diagnostic and therapeutic interventions to promote recovery AKI affects one in five hospitalized patients and is associated with poor short-term and long-term clinical and patient-centered outcomes. Among those who survive to discharge, significant gaps in documentation, education, communication, and follow-up have been observed. The American Society of Nephrology established the AKINow taskforce to address these gaps and improve AKI care. The AKINow Recovery workgroup convened two focus groups, one each focused on dialysis-independent and dialysis-requiring AKI, to summarize the key considerations, challenges, and opportunities in the care of AKI survivors. This article highlights the discussion surrounding care of AKI survivors discharged without the need for dialysis. On May 3, 2022, 48 patients and multidisciplinary clinicians from diverse settings were gathered virtually. The agenda included a patient testimonial, plenary sessions, facilitated small group discussions, and debriefing. Core challenges and opportunities for AKI care identified were in the domains of transitions of care, education, collaborative care delivery, diagnostic and therapeutic interventions, and digital health applications. Integrated multispecialty care delivery was identified as one of the greatest challenges to AKI survivor care. Adequate templates for communication and documentation; education of patients, care partners, and clinicians about AKI; and a well-coordinated multidisciplinary posthospital follow-up plan form the basis for a successful care transition at hospital discharge. The AKINow Recovery workgroup concluded that advancements in evidence-based, patient-centered care of AKI survivors are needed to improve health outcomes, care quality, and patient and provider experience. Tools are being developed by the AKINow Recovery workgroup for use at the hospital discharge to facilitate care continuity.

The elite implementations of the trihybrid nanoliquid are converting solar thermal energy, photovoltaic heat collectors, and power storage. This work explores the irreversibility analysis in an upright microchannel subjected to convective ternary hybrid nanoliquid flow with quadratic thermal radiation. The consequences of the Darcy Forchheimer rule, heat source coefficient, and no slip condition on the thermal performance are studied. In the base liquid engine oil, the three distinct types of nanoparticles having different shapes including spherical (Ti), cylindrical (MgO) and platelet (Cu) configurations were considered in this scrutiny. The combined model is used to approach trihybrid and hybrid nanoliquid thermophysical properties. The governing partial differential equation is transferred into the ordinary differential equation by employing dimensionless terms. In the next step to solve the ordinary differential equations, the RKF45 numerical technique is used. The acquired upshots divulge that the improved temperature parameter strongly supports the flow profile. Thermal profile inflates with increased Darcy number. The entropy production shows dual behaviour for the escalated quadratic radiation parameter. With an enhanced darcy number of 400%, the entropy generation rises by 14% in the ternary hybrid nanoliquid circumstances and rises by 11% in the hybrid nanoliquid situation. In addition, when the combination of spherical, cylindrical, and platelet nanoparticles is considered the rate of thermal transfer is greater compared to spherical and cylindrical nanoparticle combinations.

Abstract In recent times, the number of malware on Android mobile phones has been growing, and a new kind of malware is Android ransomware. This research aims to address the emerging concerns about Android ransomware in the mobile sector. Previous studies highlight that the number of new Android ransomware is increasing annually, which poses a huge threat to the privacy of mobile phone users for sensitive data. Various existing techniques are active to detect ransomware and secure the data in the mobile cloud. However, these approaches lack accuracy and detection performance with insecure storage. To resolve this and enhance the security level, the proposed model is presented. This manuscript provides both recognition algorithms based on the deep learning model and secured storage of detected data in the cloud with a secret key to safeguard sensitive user information using the hybrid cryptographic model. Initially, the input APK files and data are preprocessed to extract features. The collection of optimal features is carried out using the Squirrel search optimization process. After that, the Deep Learning-based model, adaptive deep saliency The AlexNet classifier is presented to detect and classify data as malicious or normal. The detected data, which is not malicious, is stored on a cloud server. For secured storage of data in the cloud, a hybrid cryptographic model such as hybrid homomorphic Elliptic Curve Cryptography and Blowfish is employed, which includes key computation and key generation processes. The cryptographic scheme includes encryption and decryption of data, after which the application response is found to attain a decrypted result upon user request. The performance is carried out for both the Deep Learning-based model and the hybrid cryptography-based security model, and the results obtained are 99.89% accuracy in detecting malware compared with traditional models. The effectiveness of the proposed system over other models such as GNN is 94.76%, CNN is 95.76%, and Random Forest is 96%.

Objectives: Robotic-assisted surgery (RAS) is a minimally invasive technique practiced in multiple specialties. Standard training is essential for the acquisition of RAS skills. The cost of RAS is considered to be high, which makes it a burden for institutes and unaffordable for patients. This systematic literature review (SLR) focused on the various RAS training methods applied in different surgical specialties, as well as the cost elements of RAS, and was to summarize the opportunities and challenges associated with scaling up RAS. Methods: An SLR was carried out based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses reporting guidelines. The PubMed, EBSCO, and Scopus databases were searched for reports from January 2018 through January 2024. Full-text reviews and research articles in the English language from Asia-Pacific countries were included. Articles that outlined training and costs associated with RAS were chosen. Results: The most common training system is the da Vinci system. The simulation technique, which includes dry-lab, wet-lab, and virtual reality training, was found to be a common and important practice. The cost of RAS encompasses the installation and maintenance costs of the robotic system, the operation theatre rent, personnel cost, surgical instrument and material cost, and other miscellaneous charges. The synthesis of SLR revealed the challenges and opportunities regarding RAS training and cost. Conclusions: The results of this SLR will help stakeholders such as decision-makers, influencers, and end users of RAS to understand the significance of training and cost in scaling up RAS from a managerial perspective. For any healthcare innovation to reach a vast population, cost-effectiveness and standard training are crucial.

In this work, we report the effects of Thompson, Troian slip, and Stefan blowing on the magnetohydrodynamic (MHD) Cassonnanofluid behavior via a porous media while a chemical reaction is taking place. We also examine the effects of radiation parameters, Joel heat, and velocity distribution using a two-phase model for nanofluids. Similarity transformations may be used to convert the primary Partial Differential Equations (PDEs) into Ordinary Differential Equations (ODEs). MATLAB Shooting and Runge-Kutta algorithms may be used to solve nonlinear equations. The variations in non-dimensional parameters show the effects on mass transfer, heat, and fluid flow properties. It is shown that the skin friction coefficient falls as the Stefan blowing parameter S increases. As the values of the Thompson and Troian slip parameters increase, the fluid concentration decreases. With an increase in Nt, Nb, and k, the fluid's heat rises but its concentration falls. The results of this analysis provide several enticing aspects that are going to give merits for further study of the problems.

Satellite communication has reached a turning point in transmitting data quickly and receiving signals clearly in faraway places. This paper presents a metamaterial-based antenna for distant and military applications that overcomes restrictions and performs unmatched. This project explores metamaterials to create a unique antenna. Expect to be astounded by its power to bend electromagnetic waves, miniaturize, and change our relationship with the universe. The feeding mechanism of this antenna is a modified co-planar waveguide. At 5.6 GHz, the proposed antenna unit cell measures 0.076λ0×0.2λ0, making it small thanks to its MTM characteristic. CP radiation is made when two orthogonally polarized modes are stimulated at the same time and two composite right- and left-handed transmission line unit cells are placed orthogonally. Reduced size, increased bandwidth, and improved Learn how this discovery opens new doors for high-resolution photography, broadband internet, and space exploration. One nanostructure that makes up a metamaterial is the Split Ring Resonator (SRR). SRR dimensions must be smaller than the resonance wavelength, making them crucial for near-infrared and optical responses. This effort examined nanoscale SRR characteristics in the infrared and visible ranges. SRRs composed of aluminum (Al) and gold (Au) were produced on silicon and silica substrates using electron beam lithography (EBL). The proposed structure consists of a microstrip-line-supplied SRR, a parasitic patch that is perpendicular to the ground plane, and two via holes connected by two split rings that feed the patch indirectly. Choosing the right material parameter for all such antennas depends on the planned structure. Fabrication and testing of the antenna matched simulations. Dimensions: 23.7 mm × 16.2 mm × 1.6 mm; substrate dielectric constant: 4.4. Experimental data are detailed and compared to analytical calculations. Simulation results show that metamaterials have negative permittivity and permeability in a certain frequency range, which can be predicted by an analytical method. Simulations indicated eight BRS, WiMAX, radar, and mobile phone resonance frequencies. Radiation is dipole-like in the omnidirectional in the H-plane and E-plane. 10 dB return loss and 3 dB axial ratio bandwidths are 38.6.7% and 8.1%, respectively.

Abstract High-precision additive manufacturing technologies, such as two-photon polymerization, are mainly limited to photo-curable polymers and currently lacks the possibility to produce multimaterial components. Herein, we report a physically bottom-up assembly strategy that leverages capillary force to trap various nanomaterials and assemble them onto three-dimensional (3D) microscaffolds. This capillary-trapping strategy enables precise and uniform assembly of nanomaterials into versatile 3D microstructures with high uniformity and mass loading. Our approach applies to diverse materials irrespective of their physiochemical properties, including polymers, metals, metal oxides, and others. It can integrate at least four different material types into a single 3D microstructure in a sequential, layer-by-layer manner, opening immense possibilities for tailored functionalities on demand. Furthermore, the 3D microscaffolds are removable, facilitating the creation of pure material-based 3D microstructures. This universal 3D micro-/nanofabrication technique with various nanomaterials enables the creation of advanced miniature devices with potential applications in multifunctional microrobots and smart micromachines.

The problematisation of a social phenomenon is a political process that both constructs the problem and, in doing so, suggests possible remedies and occludes others. Based on the first-ever comprehensive analysis of 129 UK university policies to address Gender-based violence (GBV), we examine how the 'problem' of GBV is conceptualised in institutional policies. We explore three interconnected themes: the nature of the 'problem' that is constituted, analysing whether GBV is explicitly acknowledged and constructed narrowly or broadly; the place of gender and its intersection with other social relations of power in this problematisation; and the implicit ways in which GBV is constructed as an individual or a social problem. We also examine the implications of such constructions for imagining possible responses to GBV. In doing so, this article contributes to academic debates on the conceptualisation of GBV, while offering original insights into how such conceptualisations are materialised within institutional policy and regulatory frameworks.

Mobile Augmented Reality (MAR) applications enhance user experiences by providing realistic information about the current location through mobile devices. However, MAR applications are computationally intensive, leading to high energy consumption and latency issues. To address these challenges, this research presents a Hybrid Monte Carlo Tree Search (HMCTS) based task offloading scheme, combining a genetic algorithm with Monte Carlo tree search for efficient task management. The proposed method uses YoloV7 for object recognition and aims to reduce energy consumption, response time, and migration time. Experimental results demonstrate that the HMCTS approach significantly reduces energy consumption to 1290 kJ, response time to 24 ms, and migration time to 0.52 ms, outperforming existing techniques. These improvements highlight the potential of the HMCTS method for enhancing the performance of MAR applications. Proposed hybrid approach aims to improve the efficiency and effectiveness of task offloading in MAR applications. The HMCTS model dynamically offloads tasks to edge servers, optimizing scheduling time, response time, and energy consumption.