Search Results (14,476)

Since extracorporeal membrane oxygenation (ECMO) is primarily used for patients in a high-risk state and is an invasive procedure, its unique application scenarios make it difficult to recruit suitable cases for clinical trials. Therefore, large animal models have become one of the most important models for preclinical evaluation of the safety and effectiveness of ECMO. This study aims to assess the safety and performance of a novel portable ECMO device with Small-tail Han sheep. Fifteen sheep were divided into a test group (LIFEMOTION, Chinabridge, Shenzhen, China) and control group (NOVALUNG XLUNG kit 230, Xonis, Heilbronn, Germany) with veno-venous ECMO (VV-ECMO) and veno-arterial ECMO (VA-ECMO) modes. Tracheal intubation, arteriovenous access, and ECMO support were performed. Vital signs and blood laboratory tests of the subjects were monitored and recorded. The main organs were examined pathologically at the end of day fourteen. The serum protein expression profile was analyzed by protein quantification techniques. All sheep were successfully weaned from ECMO without transfusion or cannula complications. No significant differences were observed between the two groups in terms of vital signs, oxygenation, hemodynamic stability, and physiological function (p > 0.05). According to the serum protein expression profile, no significant biomarkers associated with ECMO clinical complications were identified. The LIFEMOTION ECMO device demonstrated good safety and efficacy. Full article

Background/Objectives: Chronic retinal diseases usually require repetitive local dosing. Depending on factors such as dosing frequency, mode of administration, and associated costs, this can result in poor patient compliance. A better alternative involves using controlled-release drug delivery systems to reduce the frequency of intravitreal dosing and extend drug release. However, reaching the market stage is a time-consuming process. Methods: In this study, we employed two computational approaches to model and estimate the parameters governing the diffusion-controlled drug release from bi-layered microspheres. The case study involved microspheres composed of a chitosan core and a polycaprolactone (PCL) shell. The model drugs were bovine serum albumin and bevacizumab (an agent that slows neovascularization due to retinal disorders). Drug release from the microspheres is described by a mathematical model that was solved numerically using the finite difference and the finite element approaches. The parameter estimation was performed by nonlinear least-squares regression. Results: We used the estimated parameters to simulate the cumulative release under various conditions and optimize the device design to guide future experimental efforts and improve the duration of release beyond a target daily therapeutic release rate from the microspheres. Conclusions: We investigated the effects of polymeric layer sizes on drug release and provided recommendations for optimal sizes. We provide straightforward computational tools for others to reuse in designing bi-layered microspheres for intravitreal drug delivery needs in the treatment of chronic ocular neovascularization. Full article

Many gesture recognition systems with innovative interfaces have emerged for smart home control. However, these systems tend to be energy-intensive, bulky, and expensive. There is also a lack of real-time demonstrations of gesture recognition and subsequent evaluation of the user experience. Photovoltaic light sensors are self-powered, battery-free, flexible, portable, and easily deployable on various surfaces throughout the home. They enable natural, intuitive, hover-based interaction, which could create a positive user experience. In this paper, we present the development and evaluation of a real-time, hover gesture recognition system that can control multiple smart home devices via a self-powered photovoltaic interface. Five popular supervised machine learning algorithms were evaluated using gesture data from 48 participants. The random forest classifier achieved high accuracies. However, a one-size-fits-all model performed poorly in real-time testing. User-specific random forest models performed well with 10 participants, showing no significant difference in offline and real-time performance and under normal indoor lighting conditions. This paper demonstrates the technical feasibility of using photovoltaic surfaces as self-powered interfaces for gestural interaction systems that are perceived to be useful and easy to use. It establishes a foundation for future work in hover-based interaction and sustainable sensing, enabling human–computer interaction researchers to explore further applications. Full article

In previous research, the group proposed a low-cost seismic isolation device, the rubber sand core composite block (RSMCB). This study builds upon newly conducted experiments to elucidate the vertical bearing capacity of the RSMCB through discrete element method (DEM) simulations. The effects of rubber content, cover plate forms, dimensions, and sidewall lengths are examined. A formula for vertical bearing capacity was derived from experimental and simulation results. The main findings are as follows: (1) The RSMCB exhibits nonlinear behavior under vertical loads. Sand and rubber particles have rough surfaces, leading to strong interparticle friction. This friction facilitates the formation of robust force chains. When the applied force is within the friction angle range, particles enter a self-locking state, ensuring stability and enabling RSMCB to withstand substantial vertical loads. (2) Higher rubber content increases pores in the RSMCB, resulting in greater vertical displacement of the cover plate. Employing a larger cover plate mitigates the vertical displacement. (3) When utilizing a square straight-cylinder cover plate for the RSMCB, its load-bearing capacity is increased by 187% compared to a square cover plate. (4) At a rubber content of 50%, minimal contact occurs between sand particles. Rubber particles control overall strength. (5) Theoretical formulas can be employed directly to ascertain the bearing capacity of the RSMCB. Full article

Introduction: High performance colonoscopy requires the monitoring of an individual’s adenoma detection rate (ADR). The Endocuff (EndoCuff Vision, Olympus America Inc., Center Valley, PA, USA) is an endoscopic distal attachment device that increases surface area exposure during colonoscopy. While studies have shown that Endocuff increased ADR, real-world data is limited on its effectiveness. Methods: The Premiere Health Database was reviewed from 2018 to 2021 to identify patients 50 years of age or older who had a screening colonoscopy. A keyword search for “Endocuff” was used to determine if Endocuff was utilized, and ICD10 codes were analyzed to determine if a polyp was found. Our primary outcome was a polyp detection rate (PDR) for Endocuff-assisted colonoscopy (EAC) and standard colonoscopy (SC). Secondary outcomes included an estimated adenoma detection rate (eADR). Logistic regression modeling was performed to examine the difference in PDR between the EAC and SC groups after controlling for baseline characteristics, insurance type, and provider experience. Results: Gastroenterologists performed 893,560 screening colonoscopies, of which 0.7% were Endocuff-assisted, while surgeons performed 234,962 screening colonoscopies and 0.5% were Endocuff-assisted. PDR was higher with EAC for both gastroenterologists (72.0% vs. 57.4%) and surgeons (55.6% vs. 43.7%), with eADR following similar trends. The odds ratio of polyp detection with vs. without Endocuff was 1.91 for gastroenterologists and 1.62 for surgeons. After adjusting for patient and provider factors, the adjusted odds ratios are 2.01 and 1.61, respectively. Conclusions: While Endocuff utilization remains low, this large study using real-world data demonstrates the ability to improve eADR by over 10% compared to standard colonoscopy. Full article

Chemical sensors have undergone transformative advances in recent years, driven by the convergence of nanomaterials, advanced fabrication strategies, and state-of-the-art characterization methods. This review emphasizes recent developments, with particular attention to progress achieved over the past decade, and highlights the role of the United States as a major driver of global innovation in the field. Nanomaterials such as graphene derivatives, MXenes, carbon nanotubes, metal–organic frameworks (MOFs), and hybrid composites have enabled unprecedented analytical performance. Representative studies report detection limits down to the parts-per-billion (ppb) and even parts-per-trillion (ppt) level, with linear ranges typically spanning 10–500 ppb for volatile organic compounds (VOCs) and 0.1–100 μM for biomolecules. Response and recovery times are often below 10–30 s, while reproducibility frequently exceeds 90% across multiple sensing cycles. Stability has been demonstrated in platforms capable of continuous operation for weeks to months without significant drift. In parallel, additive manufacturing, device miniaturization, and flexible electronics have facilitated the integration of sensors into wearable, stretchable, and implantable platforms, extending their applications in healthcare diagnostics, environmental monitoring, food safety, and industrial process control. Advanced characterization techniques, including in situ Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS, Atomic Force Microscopy (AFM), and high-resolution electron microscopy, have elucidated interfacial charge-transfer mechanisms, guiding rational material design and improved selectivity. Despite these achievements, challenges remain in terms of scalability, reproducibility of nanomaterial synthesis, long-term stability, and regulatory validation. Data privacy and cybersecurity also emerge as critical issues for IoT-integrated sensing networks. Looking forward, promising future directions include the integration of artificial intelligence and machine learning for real-time data interpretation, the development of biodegradable and eco-friendly materials, and the convergence of multidisciplinary approaches to ensure robust, sustainable, and socially responsible sensing platforms. Overall, nanomaterial-enabled chemical sensors are poised to become indispensable tools for advancing public health, environmental sustainability, and industrial innovation, offering a pathway toward intelligent and adaptive sensing systems. Full article

Efficient development of bottom-water reservoirs is seriously affected by low recovery due to the rapid rise in water content in horizontal wells. In order to cope with this problem, a number of water control devices (including ICD and AICD) have been installed in horizontal wellbores in recent years. These are used in conjunction with packers to achieve the effect of balancing the fluid production profile and controlling water in sections. As an alternative to packers, the method of horizontal-well gravel packing has been widely used. This technique utilizes the permeability of gravel to block axial flow in the annulus of the horizontal wellbore, and uses water control devices for the purpose of sectional flow restriction. In this paper, a coupled method of numerical simulation of the production dynamics of gravel-packed water-control completions in horizontal wells in bottom-water reservoirs is proposed, which can consider multi-phase flows in porous media, in layers packed with gravel particles, and in water control devices simultaneously. In order to obtain the blocking capacity of the layer packed with gravel, we built an experimental setup of the same size as the borehole and annulus of a horizontal well, tested the permeability of the layer using Darcy’s law, and applied it to a coupled numerical simulation model. After comparison with actual well examples, it was proved that the coupled numerical simulation model has good accuracy, and can be used to carry out production predictions for gravel-packed water-control completions in horizontal wells in bottom-water reservoirs. The study also provides field engineers with a design tool for parameter optimization using a different water control method. Full article

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article

Distributed denial-of-service (DDoS) attacks are a prevalent threat to resource-constrained IoT deployments. We present an edge-based detection and mitigation system integrated with the oneM2M architecture. By using a Raspberry Pi 4 client and five Raspberry Pi 3 attack nodes in a smart-home testbed, we collected 200,000 packets with 19 features across four traffic states (normal, SYN/UDP/ICMP floods), trained Decision Tree, 2D-CNN, and LSTM models, and deployed the best model on an edge computer for real-time inference. The edge node classifies traffic and triggers per-attack defenses on the device (SYN cookies, UDP/ICMP iptables rules). On a held-out test set, the 2D-CNN achieved 98.45% accuracy, outperforming the LSTM (96.14%) and Decision Tree (93.77%). In end-to-end trials, the system sustained service during SYN floods (time to capture 200 packets increased from 5.05 s to 5.51 s after enabling SYN cookies), mitigated ICMP floods via rate limiting, and flagged UDP floods for administrator intervention due to residual performance degradation. These results show that lightweight, edge-deployed learning with targeted controls can harden oneM2M-based IoT systems against common DDoS vectors. Full article

This paper presents a proof-of-concept real-time safety alerting system for safety-critical environments such as construction sites. Key components of the system include Bluetooth Low Energy (BLE) devices for indoor localization, integrated with a customized Android application using the Framework for Internal Navigation and Discovery (FIND). Administrative control and data management are handled by a server-side component, supported by an interactive website for real-time safety monitoring. The architecture supports safety zoning and employs machine learning algorithms, including k-NN, Random Forest, and SVM, for analyzing localization data. Experimental validation in a laboratory setup demonstrates a localization accuracy of 97%, a response time of 1.2 s, and a maximum spatial error of 1.2 m. These results highlight the system’s reliability and potential for enhancing safety compliance in real-world deployment scenarios. Full article

Spiral concentrators are gravity and centrifugal force-based devices designed for mineral concentration. During processing operations, dynamic variations in the slurry’s liquid film thickness can induce hydrodynamic instability, generating roll waves on the free surface that compromise particle separation efficiency. To ensure operational stability and efficacy, this study establishes a theoretical shallow-water flow model for slurry dynamics in spiral concentrators based on hydraulic principles. Through L₂₇(3¹³) orthogonal experiments and real-time ultrasonic film thickness monitoring, the influence of key parameters on roll wave evolution is quantified. Results indicate that roll waves follow an “instability-development-dissipation” sequence. The pitch-to-diameter ratio (P/D) exerts a highly significant effect on roll wave intensity, while particle properties (density and size) exhibit moderate significance. In contrast, feed flow rate and solid concentration show negligible impacts. Roll waves amplify fluid turbulence, triggering stochastic migration of particles (especially low-density grains), which increases the standard deviation of zonal recovery rates (ZRR) and degrades separation precision. This work provides critical insights into particle behavior under roll wave conditions and offers a theoretical foundation for optimizing spiral concentrator design and process control. Full article

This paper presents an analysis of the dynamic properties of the Rogowski coil, primarily by determining the dynamic errors for several selected test signals and the upper bound of the dynamic error for two quality criteria: the integral-square error and the absolute error. A procedure for filtering and reproducing these signals is also presented. The foundation of the presented research is an equivalent circuit model of the Rogowski coil, developed primarily for applications in electrical power and electromechanical systems. Two novel aspects of this work are the determination of dynamic errors for the Rogowski coil and a graphical and quantitative comparison of their values. The research results presented in this paper may serve as a foundation for enhancing the accuracy and dynamic reliability of both the Rogowski coil and other devices (e.g., transformers and current transformers) used in the power industry and mechanical engineering, particularly in the condition monitoring of a broad range of power equipment and in the experimental analysis of electromechanical systems operating under variable load conditions. The findings also highlight the importance of accurate current measurement in modern energy systems, where transient and high-frequency components increasingly affect performance and reliability. Consequently, the presented methodology provides a useful framework for guiding sensor selection and signal processing strategies in advanced monitoring and control applications. Full article

The white cane is a reliable and often-used assistive aid; however, it does not protect against obstacles at the head level. We designed and built an ultrasonic-based obstacle detector with a limited detection field in front of the head. The detector is located on the chest and can be mounted on backpack straps or around the neck. We have performed testing with 74 blind people and their instructors. Blind people used the device for three to four weeks in their regular lives, and instructors tested it by themselves or with their clients. The testing showed that individualization by the type of mounting is helpful. The needed detection distance depends on the situation and the speed of movement. In total, 70% of the users were satisfied with the distance options 80 cm, 110 cm, and 140 cm. 81% of the testers were satisfied, or somewhat satisfied, with the sliding switches to control. It is simple, and its position (setting) can be detected by touch. The testers see the benefit of using the device, especially in unknown environments (outdoor and indoor), primarily because of the increased safety by movement (64%) or the feeling of security (41%). Full article

The increasing incidence of hospital-acquired infections and antimicrobial-resistant pathogens poses a major clinical challenge. Nearly all medical devices are vulnerable to bacterial biofilm formation, which acts as a protective coating against the host defense systems and antibiotics. The persistence of biofilm infections, accounting for around 65% of all microbial infections, and poor conventional treatment outcomes has driven interest in alternative approaches like bacteriophage therapy. This review encompasses key aspects of biofilm biology, taking into account the clinically significant ESKAPE pathogens, and provides an in-depth analysis of the role of phage agents in biofilm control as a new biofilm control strategy. Diving deeper into the mechanisms of phage-mediated processes, the review examines how bacteriophages penetrate and disrupt biofilm architecture and evaluates current therapeutic strategies that exploit these actions, acknowledging their limitations and considering possible future directions. Full article

The application of plasma technology in agriculture has emerged as a promising approach to enhance plant health and manage microbial interactions, offering potential solutions for sustainable crop production and disease control. This study contributes to this field by exploring the effects of plasma treatments on plant physiology and microbial dynamics, with a focus on their potential to improve agricultural outcomes. This investigation aims to systematically determine optimal plasma seed treatment parameters for enhancing plant vigor and promoting beneficial microbial associations while minimizing pathogenic interactions in Arabidopsis thaliana. This study focuses on understanding the effects of various plasma treatments on chlorophyll content, root length, microbial growth, and microbial quantification in plants and microbes. The treatments involve the use of an atmospheric jet plasma handheld device, a globe plasma, and a glow discharge plasma chamber with air and argon. These treatments were applied for varying time durations from 10 s to 5 min. The results demonstrated that the globe plasma treatment for 1 min significantly enhanced chlorophyll a extraction and root length, outperforming the other treatments. Additionally, the study examined the impact of plasma on plant–microbe interactions to assess whether plasma treatments affect beneficial microbes. Plasma treatments showed minimal impact on most beneficial microbe activity, though species-specific sensitivities were observed, with Pseudomonas cedrina showing moderate growth inhibition, revealing no significant disruption to their activity. The microbial quantification assays indicated that the globe plasma treatment effectively reduced microbial counts, while combined treatment with plant and microbe plasma together did not yield significant changes. Additionally, the chlorophyll estimation of plasma-treated samples indicated that the globe plasma and atmospheric jet plasma treatments were effective in enhancing chlorophyll content, whereas the combined treatment with both plant and microbe plasma did not yield significant changes. These findings suggest that plasma treatments, especially the globe plasma, are effective in improving plant health and controlling microbial activity. Future research should focus on optimizing plasma conditions, exploring the influence of plasma parameters and the underlying mechanisms, and expanding the scope to include a wider range of plant species and microbial strains to maximize the benefits of plasma technology in agriculture. Full article