Timely primary percutaneous coronary intervention (pPCI) is the most important treatment to improve outcomes in ST-segment elevation myocardial infarction (STEMI), with a strong relationship between treatment delays and morbidity and mortality. The present study aims to define the main steps for setting up a real-time digital monitoring dashboard to improve the clinical performance of STEMI management and to evaluate the impact of its implementation on the proportion of patients receiving primary percutaneous coronary intervention (pPCI) within 90min. The set-up of the digital monitoring system required the definition of detailed algorithms for the diagnosis, treatment, and rehab/follow-up phase. For each patient with a diagnosis of STEMI included in the clinical pathway (CP) a multidisciplinary working group identified i) rules for flagging patients alongside the CP, based on specific risk scores; ii) the critical points of the CP to be monitored, such as door-to-balloon time, intensive care unit length of stay, and total hospital length of stay. An interrupted time series analysis and multivariable logistic regression models were performed to assess for changes in the outcome (pPCI within 90min) after the platform implementation, adjusting for temporal and individual confounders. After the introduction of the dashboard, the proportion of timely pPCI improved from 40% pre-implementation to 65% post-implementation. Adjusted models indicated a twofold increase in the odds of meeting the 90-minute benchmark (OR = 2.00; 95% CI: 0.99-4.12). The real-time monitoring system showed a positive impact on the timely management of STEMI, highlighting the potential for improving healthcare efficiency and patient outcomes.

ABSTRACT Transient electromagnetic (TEM) methods are widely applied to characterise subsurface geological structures relevant to groundwater management. Here, we present a TEM dataset and the resulting resistivity models (available at https://doi.org/ 10.5281/zenodo.16894039 ) from a hydrogeophysical survey in northwestern Denmark aimed at mapping buried Quaternary valley systems. The data were collected with the newly developed sTEMprofiler system, a portable offset‐loop TEM system and supplemented by stationary sTEM central‐loop soundings for deeper penetration. The sTEMprofiler enabled rapid data acquisition of 319 soundings across 300 ha in 3 days. Data were processed and inverted using spatially constrained inversion, yielding reliable resistivity models with average data residuals within the standard deviation of the measurements, and with depths of investigation of 100–150 m for the sTEMprofiler and > 300 m for the largest central‐loop configuration (depending on subsurface geology and noise conditions). The resistivity models show complex Quaternary valley infills incised into Paleogene clays, uplifted limestone and low‐resistivity zones within the limestone, likely caused by residual saline porewater. In addition to the use of the resistivity models for local geological interpretation and hydrological modelling, the dataset offers considerable reuse potential. By providing raw data, system setup files, resistivity models and residuals, it enables researchers to explore a wide range of applications, including benchmarking and comparing inversion methods (deterministic, probabilistic or machine learning approaches), performing joint inversions, integrating resistivity models into geological and hydrological studies, and training or teaching sTEM data processing and inversion workflows.

Super-resolution microscopy has become an indispensable tool for investigating molecular architectures in their native cellular environment. However, most super-resolution techniques face limitations that prevent rapid, deep imaging of live samples. Random Illumination Microscopy (RIM), based on natural laser speckle illumination, is a method of choice to overcome these challenges. RIM combines laser speckle illumination at the optical resolution with an algorithm that exploits the statistical invariance of speckle patterns. In this approach, a stack of hundreds of random speckle images is acquired using a random diffusive element and then processed to reconstruct the super-resolved optical section. The invariant statistical properties of speckle patterns, which persist even as they diffuse through biological samples, enable deep-tissue imaging. Additionally, the wide-field configuration of both illumination and detection ensures high acquisition speeds and minimal sample photodamage. Here, we present the implementation of our RIM prototype within a microscopy core facility. We describe the system setup, characterization, and optimization, identifying the key elements required for its reliable operation. As a proof of concept, we also provide biological examples demonstrating the prototype's performance in resolving subcellular structures.

Detecting and tracking object has become a critical aspect in modern automation, security setup, and robotic technologies, as these systems modifies efficiency and safety, where fast and accurate detection is critical. we present a hybrid radar detection system that combines multiple sensing technologies to results were accurate detection. It detected object within a 50 cm range. The system setup an HB100 Doppler radar module for motion and speed detection with an HC-SR04 ultrasonic sensor for accurate distance measurement. A micro-servo motor sweep 180° scanning, while an ESP32 microcontroller manages sensor data, real-time computation, visualization, and alert generation. An OLED display provides a live sweep showing the object’s distance, speed, and angular position, and a camera module provides video feed. This study shows research contributions from the past decade to identify advancement in hybrid radar-based detection systems and their application ultrasonic radar, Doppler sensor, and IoT-enabled alert models. To improve accuracy, the system includes local alerts through LEDs, a buzzer, and a speaker’s, along with remote notifications via an SMS module. An RTC module ensures accurate time stamping of events, and an actuation mechanism activates when a risk is detected. The prototype shows stable performance, with correct object tracking and reduced false alarms due to the used of several sensor calculating distance, speed, and angle information, this model enhances situational awareness.

As the clock frequency and integration density of embedded systems continue to rise, especially with the widespread use of mobile embedded devices such as smartphones and Personal Digital Assistants (PDAs), power consumption has become a critical challenge in system design. Besides, the slower slower advancement of battery technology compared to computational power makes system-level optimizations to reduce energy consumption essential for enhancing performance and increasing device longevity. Therefore, this paper explores methods for power consumption optimization in embedded systems, examining the potential for low-power solutions via the collaboration of hardware and software design. By reviewing recent research and investigating the findings of Tiwari et al., which involved simulations and typical embedded system setups, this study offers a comprehensive analysis of how to optimize power consumption from multiple angles. The results demonstrate that software-level optimizations, such as algorithm scheduling and instruction optimization, can greatly reduce system power usage. Furthermore, software-hardware co-design is identified as the key direction for the future development of low-power embedded systems, enabling high energy efficiency without compromising performance.

Fully dynamic sampling of host-guest inclusion remains difficult because conventional docking and conventional molecular dynamics simulations can sample inclusion, but crystal-like binding is typically stochastic and difficult to reproduce. Here, we introduce DFDD (Distance-Guided Fully Dynamic Docking), a cloud-ready implementation of the LB-PaCS-MD framework designed to capture inclusion processes via unbiased molecular dynamics in explicit solvent. DFDD automates system setup, parameter generation, iterative short-cycle MD sampling, and trajectory analysis within a single workflow that runs on Google Colab without any installation. Progress toward complexation is guided only by the host-guest center-of-mass distance, allowing force-free exploration of insertion pathways and enabling the recovery of both stable and transient binding modes. Using β-cyclodextrin as a representative host, DFDD reproduces experimentally observed inclusion geometries within minutes and reveals intermediate states along the insertion route. Optional coupling with pKaNET-Cloud enables pH-aware, stereochemically consistent ligand protonation states prior to simulation, supporting robust host-guest modeling. This Application Note provides a transparent and accessible platform for efficient host-guest complexation studies. The DFDD framework is publicly available at https://github.com/nyelidl/DFDD.

Robot-Assisted Versus Open Surgery in Early-Stage Breast Cancer: A Systematic Review and Meta-Analysis.

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for neurological research and therapy, but its effectiveness depends on accurate and stable coil placement. Manual localization based on anatomical landmarks is time-consuming and operator-dependent, while state-of-the-art robotic and neuronavigation systems achieve high accuracy using optical tracking with head-mounted markers and infrared cameras, at the cost of increased system complexity and setup burden. This study presents a cost-effective, markerless robotic-assisted TMS system that combines a 3D depth camera and textile capacitive sensors to assist coil localization and contact control. Facial landmarks detected by the depth camera are used to estimate the motor cortex (C3) location without external tracking markers, while a dual textile-sensor suspension provides compliant "soft-landing" behavior, contact confirmation, and coil-tilt estimation. Experimental evaluation with five participants showed reliable C3 targeting with valid motor evoked potentials (MEPs) obtained in most trials after initial calibration, and tilt-verification experiments revealed that peak MEP amplitudes occurred near balanced sensor readings in 12 of 15 trials (80%). The system employs a collaborative robot designed in accordance with international human-robot interaction safety standards, including force-limited actuation and monitored stopping. These results suggest that the proposed approach can improve the accessibility, safety, and consistency of TMS procedures while avoiding the complexity of conventional optical tracking systems.

Cloud computing's journey from its early commercial days in the 2000s to becoming a backbone of enterprise technology has demanded increasingly sophisticated operational methods to satisfy the strict uptime guarantees written into modern service contracts. The biggest cloud platform companies pull in more than 350 billion US dollars every year while keeping their systems running at 99.99% availability or better—that means downtime gets measured in minutes per year, not hours. This piece digs into how DevOps techniques get put into practice across cloud systems, zeroing in on automated building and deployment pipelines, round-the-clock monitoring backed by alarm systems and operational guides, and flexible resource management through autoscaling. Control planes handling system setup work separately from data planes that crunch and move information, which stops failures from spreading through the distributed infrastructure like dominoes. Today's deployment pipelines string together validation checkpoints—automated tests, security scans, careful rollout plans—that cut deployment risks while keeping the pace of improvements quick. Alert platforms pull together warnings from hundreds of monitoring systems, using smart routing rules and escalation procedures to slash response times when problems hit. Autoscaling tech tweaks how many computational resources get used based on what's happening right now with traffic, cutting infrastructure bills through horizontal scaling that adds more servers and vertical scaling that beefs up individual machines. When all these operational practices are combined, cloud companies will be able to fulfill hard promises of availability and maintain the process of services in a smooth flow to users all over the world.

The Low-power Gigabit Transceiver (lpGBT) is a radiation-tolerant ASIC used in high-energy physics experiments for multipurpose high-speed bidirectional serial links. In 2024, almost 270,000 lpGBTs v1 were tested with a production test system that exercises the entire ASIC functionality to ensure its correct operation. Furthermore, qualification tests (Total Ionizing Dose, Single-Event Upsets, etc.) were done on lpGBTs from each production lot. Despite the thorough production and qualification tests, a design issue named “stuck at power-up” was discovered, affecting a maximum of 0.9% of delivered devices simultaneously. The test system setup developed for the characterisation of this behaviour and the results obtained are presented here.

Transradial neurointervention (TRN) benefits patients and neurointerventionalists. The radial artery is small in diameter and prone to vasospasm; thus, it must be handled gently from puncture to hemostasis. Ultrasound-guided puncture is recommended. Normal variations, such as the brachioradial artery or radial artery loop, should be considered. The size of the catheter system was selected based on radial artery diameter. The sheath-to-artery ratio must be ≤1, particularly with large-bore catheter systems or sheathless technique. The difficulty of TRN and system stability vary depending on the transradial approach side, aortic arch type, target vessel, including the common carotid and vertebral arteries, and proximal target vessel trajectory. The optimal system setup should be determined before TRN. Hemostasis should be performed using a hemostatic device, applying minimal compression and completing hemostasis within 120 min to minimize the risk of radial artery occlusion.

Developing tracking in various applications motivates researchers to explore this field. In this work, an intelligent system is suggested to automatically detect moving colored balls in real time and calculate the speed and direction of these balls based on the deep learning algorithm RCNN. The data is compared with the Alex-net algorithm because it is a standard method. The proposed algorithm is one of the machine learning algorithms based on the principle of training and learning, which relies on the sequential classifier. The proposed system consists of a phone camera, colored balls, and different environmental lighting (changing from one to eight lights). There are two luxmeters used to measure the intensity of light. Four parameters are measured to evaluate the performance of algorithms and system setup: accuracy, average time, detection ratio, and speed. The best class and training were selected and approved for detecting the blue and green balls. This proposed algorithm can be used to detect any moving object. Results showed a high quality of ball detection and tracking with almost 100% accuracy.

A significant share of steam for industrial, communal and household needs is currently produced by boiler plants of various types. A significant part of such facilities is located in close proximity to industrial enterprises and oil refineries (refineries), which determines the specifics of the fuel used. Steam generating plants and boiler units often operate on gas generated as a result of oil refining processes. Unlike natural gas, such fuel gas is characterized by an unstable composition and the presence of various impurities that significantly affect the parameters and quality of the combustion process.One of the main disadvantages of using fuel gas of this type is the presence of sulfur compounds. During combustion, sulfur compounds are formed, which under certain thermodynamic conditions are capable of converting to sulfuric acid. Acid condensation on heating surfaces leads to intensive corrosion of metal structures, reducing equipment life and increasing operating costs.The article is devoted to the study of the process of condensation of flue gases on the walls of the tail heating surfaces of the boiler, in particular on the tube bundles of the air heater. In the course of the work, the fact of the formation and condensation of sulfuric acid in the air heater zone was confirmed, as well as experimental data characterizing the beginning of condensation and the peculiarities of heat exchange in real operating modes were presented.It has been shown that the existing measures to prevent condensation are insufficient: the area of installed air heaters for preheating the air before the air heater is limited. It is proposed to install additional heaters (three units) to increase the heat exchange area and stabilize the temperature regime. In addition, a new technical solution has been developed based on the recirculation of flue gases from the region of high temperature potential to the zone before the air heater. It is possible to control the flow of recirculated gases using a gate, which allows you to ensure the flexibility of the system setup and reduce the likelihood of acid condensation.The results obtained are in demand by designers and operating personnel of boiler houses, since they allow increasing the reliability of equipment and reducing the cost of its maintenance.

BackgroundApathy is common among older adults residing in long-term care (LTC) and impairs quality of life for both older adults and care providers. Few pharmacological remedies exist, and nonpharmacologic approaches that engage those with apathy require extensive personnel time. Thus, technological approaches have been encouraged, including virtual reality (VR) and socially assistive robots (SAR). Despite a growing interest in their use, input from older adults and staff is often absent in their design. Involving older adults in the development of interactive health technologies is necessary to enhance the functionality, usability, and likelihood of promoting the intended health outcomes.ObjectiveWe aimed to design and evaluate SAR and nonimmersive VR (SAR-VR) activities for pairs of older adults that would encourage human-to-human interaction, an essential activity to mitigate apathy.MethodsWe implemented a multistep, user-centered design. A humanoid and dog SAR were used in combination with nonimmersive VR activities for pairs of older adults. An interdisciplinary team of engineers, nurses, and physicians collaborated with older adults and staff to create 4 activity prototypes, 3 with the humanoid robot and 1 with the dog robot. A total of 14 older adults at 2 sites participated in the design and evaluation of the different components of the system throughout all stages. Site 1 participants were instrumental in the development, and Site 2 participants validated the prototype activities. Data were collected at each session via observations, interviews, and a 6-item questionnaire that rated their degree of comfort and confidence in (1) using the wands, (2) interacting with the robot, and (3) interacting with the nonimmersive VR environment using a 5-point Likert response. Additionally, 5 staff from Site 2 were recruited to evaluate the ease of setting up and running the system at 2 different sessions. After each session, the system setup and interface were refined based on their feedback.ResultsA total of 4 of 6 older adults (mean age 85, SD 9.3 years; 2 male) at Site 1 completed field testing development, and 8 residents (mean age 80, SD 4.7 years; 2 male) at Site 2 completed field testing validation. Participant comfort and confidence increased significantly over successive iterations of the system across most categories (Site 1: Wilcoxon signed rank test P=.03; Site 2: Wilcoxon signed rank test P<.001). Additionally, 5 LTC staff members successfully set up the system with minimal cueing from the researchers, demonstrating the usability of the system for caregivers. Iterative design changes incorporated hardware, software, and activity domains.ConclusionsThese initial results demonstrate that LTC older adults and staff are capable and critical to the development and implementation of SAR-VR activities. Future studies are needed to evaluate the feasibility of implementation and effectiveness in reducing apathy.Trial RegistrationClinicalTrials.gov NCT05178992; https://clinicaltrials.gov/study/NCT05178992

The operational range and reliability of most commercially available UAVs employed in surveillance, agriculture, and infrastructure inspection missions are limited due to the use of short-range radio frequency connections. To alleviate this issue, the present work investigates the possibility of real-time long-distance UAV control using a commercial 4G LTE network. The proposed system setup consists of a Raspberry Pi 4B as the onboard computer, connected to a Pixhawk-2.4 flight controller mounted on an F450 quadcopter platform. Flight tests were carried out in open-field conditions at altitudes up to 50 m above ground level (AGL). Communication between the UAV and the ground control station is established using TCP and UDP protocols. The flight tests demonstrated stable remote control operation, maintaining an average control delay of under 150 ms and a video quality resolution of 640×480, while the LTE bandwidth ranging from 3 Mbps to 55 Mbps. The farthest recorded test distance of around 4200 km from the UAV to the operator also indicates the capability of LTE systems for beyond-visual-line-of-sight operations. The results show that 4G LTE offers an effective method for extending UAV range at a reasonable cost, but there are limitations in terms of network performance, flight time and regulatory compliance. This study establishes essential groundwork for future UAV operations that will utilize 5G/6G and satellite communication systems.

SUMMARY Recently, semi-airborne transient electromagnetic (TEM) systems have gained attention in geophysical investigations due to their ability for fast mapping and minimal ground access requirement. These systems consist of a ground-based transmitter source and an inductive receiver coil, carried by an uncrewed aerial vehicle. This study investigates how transmitter source selection in field-based semi-airborne TEM systems affects model parameter uncertainty, using synthetic subsurface models. The simulated dB/dt responses highlight distinct signal characteristics between the galvanic-based system (herein galvanic source) and the inductive-based system (herein inductive source), with differences observed across varying subsurface conditions. An analysis of four synthetic 3-layer models highlights that the inductive-based system resolves shallow conductors better at short offsets, whereas the galvanic-based system is better at resolving parameters for deeper targets at large offsets. Both systems, however, face challenges in accurately resolving resistive targets embedded between conductors, highlighting the need for strategic selection of the transmitter source. The galvanic-based system consistently achieves a higher signal-to-noise ratio (SNR), particularly at large offsets, making it better suited for deep exploration. In contrast, the inductive-based system exhibits lower SNR, higher noise susceptibility, and sign-changing dB/dt responses at increasing offsets adding complexity to data processing and interpretation. Despite these limitations, inductive-based systems enable earlier time measurements with signal magnitudes at short offsets comparable to galvanic-based, due to shorter current turn-off times. In this analysis, we have used two system setups utilizing inductive and galvanic sources that reflect commonly used systems, but obviously, assumptions regarding transmitter characteristics such as type, size, waveform and current amplitude, will influence the results when examining details more closely.

Abstract. The Mount Brown South ice core (MBS 69.111° S 86.312° E) is a new, high resolution ice core drilled in coastal East Antarctica. With mean annual accumulation estimated to be ∼ 30 cm ice equivalent throughout the length of the core (∼ 290 m), MBS represents a high resolution archive of ice core data spanning 1137 years (873–2008 CE), from an area previously underrepresented by high resolution ice core data. Here, we present a high-resolution dataset of chemistry and impurities obtained via continuous flow analysis (CFA). The dataset consists of meltwater electrolytic conductivity, sodium (Na+), ammonium (NH4+), hydrogen peroxide (H2O2), and insoluble microparticle measurements. The data are presented in three datasets: as a 1 mm depth resolution record, 3 cm averaged record, and decadal average record. The 1 mm record represents an oversampling of the true resolution, as due to smoothing effects the actual resolution is closer to 3 cm for some species. Therefore, the 3 cm resolution dataset is considered to be the minimum true resolution given the system setup. We also describe the current Copenhagen CFA system, and provide a detailed assessment of data quality, precision, and functional resolution. The 1 mm averaged, 3 cm averaged, and MBS2023 decadal averaged datasets are available at the Australian Antarctic Data Center: https://doi.org/10.26179/9tke-0s16 (Harlan et al., 2024).

Quantum digital signatures (QDS) establish a framework for information-theoretically secure authentication in quantum networks. As a specialized extension of QDS, quantum proxy signatures facilitate secure delegation of signing privileges in distributed quantum environments. However, existing schemes require the predefinition of verifier identities at the system setup phase, which fundamentally constrains their deployment in real-world scenarios. To address this constraint, we propose a quantum proxy signature scheme supporting verification by arbitrary parties without pre-registration while maintaining information-theoretic security guarantees. This work presents a constructive approach to mitigating verification constraints in quantum proxy signature architectures.

This paper presents subcutaneous and continuous blood pressure (BP) monitoring using aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducers (PMUTs) in an ambulatory sheep. A 37 times 45 PMUTs array with a footprint of 5 times 5 mm2 has been designed and fabricated as a prototype device. The deep reactive ion etching (DRIE) process to open the backside holes on the silicon substrate has been optimized to create active device diaphragms with a radius of 29 μm. The resulting PMUT unit has a measured resonant frequency of 6.5 MHz in water, an output acoustic pressure of 28 kPa at a distance of 10 mm, and a 6-dB bandwidth of about 33%. The BP monitoring scheme is validated through both in vitro and in vivo experiments to illustrate the correlation between the diameter of the blood vessel and pressure. Simulations indicate that possible issues in misalignment between the device and the blood vessel can result in a 60% reduction in signal strength with only 1 mm in misalignment. This highlights the advantage of subcutaneous implantation in maintaining a stable interface and consistent alignment for reliable long-term BP monitoring, in contrast to similar approaches via wearable system setups. The in vivo testing result shows BP wave fine features such as dicrotic notches and the averaged systolic/diastolic pressure errors are −1.2 pm 2.1 and −2.9pm1.4 mmHg, respectively, which meets the clinical standard as calibrated by a gold-standard arterial line pressure sensor. As such, this system highlights the potential applications in silent, continuous, and highly accurate BP monitoring for hypertension patients using this implantable MEMS-based technology.

Roll misalignment in roll-to-roll (R2R) processes is a critical cause of lateral displacement and tension imbalance, leading to dimensional instability and surface defects in polymer films. Conventional analyses based on beam or camber models often require complex calibration and additional sensing, which can limit their applicability in real-time production environments. This study introduces a diagnostic approach that estimates web misalignment directly from side-to-side tension differences measured in roll-to-roll (R2R) systems. The method eliminates the need for additional sensors and complex geometric calibration, simplifying system setup. The correlation between tension imbalance, lateral displacement, and the equivalent misalignment angle was experimentally established. Our approach produced accurate predictions across various process conditions, including different roll misalignments and applied tensions, and we found that reducing tension “hunting” further enhances prediction stability. This study demonstrates that the proposed tension-based approach can complement existing systems and reduce the reliance on complex external sensing for diagnostic checks of misalignment. By simplifying alignment diagnostics, the method provides a practical route to enhance process setup, reduce downtime, and improve the uniformity of polymer films in continuous manufacturing.