Applicability Of System Research Articles

Prediction of complications after laparoscopic partial nephrectomy: feasibility of E‑PASS score.

With significant advancements in laparoscopic procedures and tools over the last decade, the partial nephrectomy (PN) surgical approach has gradually transformed from open to laparoscopic partial nephrectomy (LPN). Although numerous studies evaluating the postoperative complications of laparoscopic partial nephrectomy can be found in the literature, a scoring system that addresses both preoperative physical condition and intraoperative risk factors has not yet been established. The Estimation of Physiological Ability and Surgical Stress (E-PASS) score is a scoring system that considers both the patient's preoperative condition and intraoperative variables to predict the risk of postoperative complications in surgically treated patients. This study aimed to assess the applicability of the E-PASS scoring system for predicting postoperative complications following LPN. We analyzed data of 196 patients who underwent LPN between 2017 and 2024 by a single surgeon in this single-center retrospective study. Demographic data of the patients, parameters reflecting patients' preoperative physical condition, and intraoperative risk factors were recorded. Postoperative complications were classified using the Clavien-Dindo system. The E-PASS score and its sub-scores were calculated for each patient. Various degrees of complications developed in 46 patients (23.5%). Patients who developed complications had significantly higher mean age, performance scores, tumor sizes, length of hospital stay, and E-PASS values. The cutoff value of the E-PASS Comprehensive Risk Score (CRS) for predicting the development of postoperative complications was determined to be -0.1692 (AUC = 0.676; 95% CI 0.581-0.771; p < 0.001). According to the results of the multivariate analysis, the presence of E-PASS CRS > -0.1692 (OR 4.872; 95% CI 2.384-9.957; p < 0.001) and tumor size (OR 1.021; 95% CI 1-1.041; p = 0.047) was identified as independent risk factors predicting postoperative complications. Patients with a CRS higher than the cutoff value had a 4.87 times higher rate of postoperative complications after LPN. The E-PASS scoring model successfully predicts postoperative complications in patients undergoing LPN by utilizing preoperative data on the patient's physical condition and surgical risk factors. The E-PASS score and its sub-scores can serve as objective criteria to determine the risk of postoperative complications both preoperatively and immediately postoperatively.

Global view of haematolymphoid tumor classifications and their application in low- and middle-income countries.

The accurate diagnosis of haematolymphoid malignancies is crucial for effective cancer care, but major obstacles to diagnosis exist in low- and middle-income countries (LMICs). This article explores the global applicability of current haematolymphoid classification systems, which are predominantly derived from data generated in high-income countries (HICs). Although disproportionately burdened with poor cancer outcomes, LMICs are generally faced with limited diagnostic resources, suboptimal access to therapeutics, and inadequate healthcare infrastructure. The article highlights the challenges faced by LMICs, including inconsistent access to high-quality pathology services, limited availability of advanced diagnostic techniques, and a lack of population-based cancer registry data. It also discusses the progress made in narrowing the gap between LMICs and HICs, such as the introduction of resource-adapted classifications, improved guidance on essential diagnostic tools, and strengthening of in-country professional pathology networks. Innovative diagnostic approaches, including gene expression profiling and machine learning, represent potential solutions for improving the diagnostic accuracy in LMICs, but addressable gaps remain. Recommendations are suggested for sustainable investments in diagnostic infrastructure, capacity-building, and population-based cancer registries to enhance the global applicability of haematolymphoid classification systems and improve outcomes for patients in LMICs.

GNSS-assisted optimal alignment method for low-cost SINS motion of vehicle

Abstract To improve the alignment accuracy and environmental applicability of the micro-electromechanical system (MEMS)-based strapdown inertial navigation system (SINS), this study proposes a global navigation satellite system (GNSS)-assisted multi-vector determination of the attitude optimal indirect coarse alignment. Using the inertial information output from the inertial measurement unit and the velocity information from the GNSS, a simplified velocity observation vector is constructed and the velocity lever arm stemming from the mounting position inconsistencies of the GNSS and SINS is fed back into the velocity of the carrier system. When constructing the observation vectors, the integration interval is shortened by reconstructing the two-vector integration formula for reducing the cumulative error of the inertial device. The attitude matrix problem is defined as the Wahba problem, which is solved using the singular value decomposition method. Based on the relationship between gyro zero bias and misalignment angle, the corresponding state and measurement equations are designed. Furthermore, owing to the measurement noise uncertainty, the adaptive traceless Kalman filtering algorithm is introduced to realize the effective adaptation processing of the measurement noise. More accurate attitude matrix estimates are obtained by continuously correcting the carrier system transformation matrix. The running car experiment results show that the proposed method exhibits higher alignment accuracy and environmental applicability than the current MEMS strapdown inertial navigation coarse alignment method and the traditional optimization-based alignment method.

Polarization-Enhanced Underwater Laser Range-Gated Imaging for Subaquatic Applications.

Laser range-gated underwater imaging technology, by removing most of the backscattering noise, can effectively increase image contrast and extend the detection range. The optical signal captured by a range-gated imaging system primarily comprises reflected light from the object and backscattered light from the surrounding water. Consequently, surfaces with low reflectivity or highly turbid water environments substantially constrain the applicability of the range-gated imaging system. To enhance the detection capability of underwater laser range-gated imaging, this paper proposes the incorporation of underwater polarized light imaging technology as an enhancement method. Based on polarization differences, backscattered light and reflected light from an object can be distinguished. Experimental results indicate that, compared to images obtained using a conventional range-gated laser imaging system, those captured with a polarization-enhanced system exhibit an increase of up to 47% for the corresponding Enhancement Measure Evaluation (EME) index. The proposed approach, which integrates polarization imaging with range-gated laser imaging, has the potential to broaden the applicability of underwater laser imaging scenarios, such as deep-sea exploration and military applications.

Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning.

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

Real-time ultrasound AR 3D visualization toward better topological structure perception for hepatobiliary surgery.

Ultrasound serves as a crucial intraoperative imaging tool for hepatobiliary surgeons, enabling the identification of complex anatomical structures like blood vessels, bile ducts, and lesions. However, the reliance on manual mental reconstruction of 3D topologies from 2D ultrasound images presents significant challenges, leading to a pressing need for tools to assist surgeons with real-time identification of 3D topological anatomy. We propose a real-time ultrasound AR 3D visualization method for intraoperative 2D ultrasound imaging. Our system leverages backward alpha blending to integrate multi-planar ultrasound data effectively. To ensure continuity between 2D ultrasound planes, we employ spatial smoothing techniques to interpolate the widely spaced ultrasound planes. A dynamic 3D transfer function is also developed to enhance spatial representation through color differentiation. Comparative experiments involving our AR visualization of 3D ultrasound, alongside AR visualization of 2D ultrasound and 2D visualization of 3D ultrasound, demonstrated that the proposed method significantly reduced operational time(110.25 ± 27.83s compared to 292 ± 146.63s and 365.25 ± 131.62s), improved depth perception and comprehension of complex topologies, contributing to reduced pressure and increased personal satisfaction among users. Quantitative experimental results and feedback from both novice and experienced physicians highlight our system's exceptional ability to enhance the understanding of complex topological anatomy. This improvement is crucial for accurate ultrasound diagnosis and informed surgical decision-making, underscoring the system's clinical applicability.

Variability in strawberry tunnels impacts fruit quality and limits melatonin effects.

Fluctuations in environmental conditions within fields and crop plant performance can greatly affect production and quality standards. These factors are particularly relevant for producers, who require sustained optimal production to profit from small margins. Fluctuations might be exacerbated at the end of the crop season, where neither of the aforementioned factors are optimal. In the present integrated study, we assess strawberries' nutritional quality and the impact of harvest timing, tunnel conditions and inter-individual variability in a Mediterranean production tunnel divided into blocks, where two harvests were performed 3 weeks apart. In addition, the effects of sprayed melatonin at the end of productive season were also evaluated. End-season harvesting negatively impacted fruit hydration, antioxidant capacity and ripening-related hormones in strawberry fruits. Additionally, tunnel distribution influenced fruit nutritional quality, with light radiation being the main variable factor disturbing antioxidant contents. Nutrients exhibited high inter-individual plant variability, accounting for 20% variation, and were strongly correlated with fruit hydration and ripening-related phytohormones. Finally, melatonin applications affected neither fruit production, nor nutritional parameters, for which the effects were masked by the intrinsic strawberry variability. Overall, the results underline the limitations of this type of application for field implementation. Fruit quality variation in strawberry fields is explained by environmental and inter-individual variability. Likewise, the implementation of regulatory molecules such as melatonin in field applications relies on crop homogeneity and might have limited applicability in heterogeneous productive systems. Consequently, identifying and reducing microclimate variability in productive fields is paramount for advancing agricultural practices to uphold unwavering standards on fruit quality. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Deep Learning-Based Decision Support System for Automatic Detection and Grading of Surface Corrosion on Galvanized Steel Sheets

Corrosion in the sheets produced leads to significant material losses, including the loss of resources, capital, labor, energy and knowledge. Corrosion control is significant for sheets produced and sent to customers in iron and steel factories. Surface corrosion testing of produced sheets and the accurate detection of corrosion levels are of great importance. The corrosion detection process for sheets in steel factories is performed visually with the naked eye. This is a subjective and time-consuming method. Identifying corrosion damage by visual detection and accurately determining the type and extent of corrosion requires expertise. Wrong decisions at this stage lead to losses during the production phase. Therefore, there is a need for systems that can automate this process and make it human-independent. In this study, a decision support system was designed to automatically detect the level of corrosion in galvanized sheets using convolutional neural networks. The average accuracy of the system is 97.5%, the average precision is 0.98, the average recall is 1 and the average F1 score is 0.99. The results we obtained indicate that a successful system has been developed for the detection and determination of corrosion levels. The high performance of the convolutional neural network models used for corrosion detection supports the practical applicability of the developed system. This system will increase the reliability and efficiency of industrial processes by enabling the accurate and automatic classification of corrosion. This system, which meets a significant need in this area for industrial organizations, reduces production costs and also makes the corrosion detection process more consistent and faster.

The ASPECT hydrocephalus system: investigating clinical applicability and system utility

PurposeHydrocephalus presents diagnostic and management challenges due to its heterogeneity. The ASPECT Hydrocephalus System, introduced in 2023, offers a comprehensive approach to describing patients with hydrocephalus. This study investigates the clinical applicability of the ASPECT Hydrocephalus System compared to the International Classification of Disease (ICD-10) and demonstrates its utility.MethodsTwo hundred pediatric and adult patients with hydrocephalus treated at Copenhagen University Hospital between September 2019 and 2020 were described according to the ASPECT Hydrocephalus System. The latest brain imaging served as assessment point.ResultsForty-seven percent of patients had more than one ICD-10 code assigned, and 40.5% of patients had an unspecific ICD-10 code as the most recent. It was possible to apply factor A (anatomy), S (symptomatology), P (previous interventions), C (complications) and T (time of onset) to all patients. Factor E (etiology) categorized 15% of patients as ‘unknown’. Combining factor A and S showed a similar incidence of acute high-pressure symptoms in patients with and without ventriculomegaly on imaging (39.7% vs 39.3%), demonstrating how symptoms and neuro-radiological findings do not necessarily correlate.ConclusionThe ASPECT Hydrocephalus System’s applicability and utility were demonstrated in a large, diverse patient population. Except for ‘Etiology’, all factors could be applied to the entire population showing the system’s robustness. While limitations in ICD-10 may force clinicians to choose between clinical measures, the ASPECT Hydrocephalus System allows comprehensive patient characterization, potentially aiding in clinical decision-making and research. Its use depends on registration quality. Application in prospective cohorts is warranted to assure feasibility.

MuHeQA: Zero-shot question answering over multiple and heterogeneous knowledge bases

There are two main limitations in most of the existing Knowledge Graph Question Answering (KGQA) algorithms. First, the approaches depend heavily on the structure and cannot be easily adapted to other KGs. Second, the availability and amount of additional domain-specific data in structured or unstructured formats has also proven to be critical in many of these systems. Such dependencies limit the applicability of KGQA systems and make their adoption difficult. A novel algorithm is proposed, MuHeQA, that alleviates both limitations by retrieving the answer from textual content automatically generated from KGs instead of queries over them. This new approach (1) works on one or several KGs simultaneously, (2) does not require training data what makes it is domain-independent, (3) enables the combination of knowledge graphs with unstructured information sources to build the answer, and (4) reduces the dependency on the underlying schema since it does not navigate through structured content but only reads property values. MuHeQA extracts answers from textual summaries created by combining information related to the question from multiple knowledge bases, be them structured or not. Experiments over Wikidata and DBpedia show that our approach achieves comparable performance to other approaches in single-fact questions while being domain and KG independent. Results raise important questions for future work about how the textual content that can be created from knowledge graphs enables answer extraction.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

Real-time monitoring and protection strategies for dense granular flow spallation target in Accelerator-Driven System

An innovative concept of a high-power, gravity-driven, dense granular spallation target has been integrated into the prototype design of the China initiative Accelerator-Driven System (CiADS), which is envisioned as a promising nuclear demonstration facility aimed at converting long-lived fission products and minor actinides into short-lived isotopes. This spallation target system, bridging the proton beam accelerator and the subcritical reactor, plays a crucial role in ensuring safety control and facilitating dynamic operational strategies. In this study, we have developed a system design for an online detector array, incorporating several neutron fission chambers and thermocouples installed in the gap between the spallation target and the subcritical reactor to monitor operations. Furthermore, we have devised real-time monitoring and protection methods to tackle the challenges of measuring beam position and the blockage condition associated with granular flow, considering the distribution of neutron flux and heat deposition under abnormal irradiation conditions. Monte Carlo simulations have demonstrated the applicability and feasibility of the control system for the dense granular spallation target in the accelerator-driven system. The numerical results confirm that the proposed control system meets the requirements for stable measurement with acceptable accuracy.

Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm

UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG–VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG–VSC algorithm’s implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.

A subchannel analysis code for advanced liquid metal fast reactor cores and study on heat transfer characteristics of core geometry parameters

The liquid metal fast reactor represents an important reactor type for the future development of nuclear energy. Accurately modeling and predicting the subchannel flow and heat transfer phenomenon in the rod bundles plays a key role in ensuring core safety. Consequently, a subchannel code suitable for the liquid metal fast reactor is analyzed based on the subchannel analysis code of the Pressurized Water Reactor (PWR). The modified code incorporates various types of liquid metals, such as lead, lead-bismuth eutectic (LBE), and sodium, along with their constitutive models, enhancing the applicability of the liquid metal reactor systems. This code has been verified and validated at several levels, including comparing the code simulation results with other similar subchannel codes results, high-dimensional Computational Fluid Dynamics (CFD) simulations, and experiment data. The sensitivity analysis of core geometric parameters and turbulent mixing coefficients is performed based on the modified version code. The influence of the core geometry, including the rod Pitch to Diameter ratio (P/D), Rod to Wall gap (RTW) and the wire wrap pitch (H) on the sensitivity of outlet temperature has been investigated. The results show that the new subchannel analysis code suitable for liquid metal cooled reactor shows reasonable agreement with the verified and validated results. The geometric parameters, such as P/D, RTW and H have noticeable effects on the outlet temperature difference of the subchannels. Additionally, the outlet temperature distribution in the subchannel is significantly affected by different turbulent mixing coefficients and the distribution of the turbulent mixing coefficient also influenced by the reactor core sizes. Overall, this study could support the rod bundles design and safety analysis in the liquid metal reactors.

Application of Vieth staging in forensic age estimation in the living using MRI of the distal radial epiphysis.

Forensic age estimation is crucial in various legal and civil contexts, particularly in regions experiencing significant migration and inadequate birth registration systems. This study evaluates the applicability of the Vieth staging system for forensic age estimation in the living using MRI of the distal radial epiphysis. A retrospective analysis was conducted on 620 left wrist MRI scans from individuals aged 9.92 to 29.58 years. The study demonstrated high intra- and inter-observer agreement values (κ = 0.974 and κ = 0.961), confirming the method's reliability. Spearman's rank correlation analysis showed significant positive correlations between age and ossification stage for both sexes. The minimum ages observed for males were 9.92 years at stage 2, 15.00 years at stage 3, 15.00 years at stage 4, 17.00 years at stage 5, and 20.00 years at stage 6. For females, the minimum ages were 10.08 years at stage 2, 12.33 years at stage 3, 14.25 years at stage 4, 16.33 years at stage 5, and 18.42 years at stage 6. The study supports the applicability of the Vieth methodology for forensic age estimation in the living and suggests that MRI could be a non-invasive and potentially effective tool for determining critical age thresholds in forensic contexts. Further research is recommended to refine these methods and explore their applicability across different populations.

Robust adaptive impedance control of coordinated electrically driven robots: An engineering application for the (p,q)-analogue of Bernstein-Stancu operators

Function approximation techniques have emerged as influential mathematical tools for designing compliant motion controllers capable of managing objects using multiple electrical manipulators without the need for exact models. However, their effectiveness often relies on having access to velocity signals, which may not always be practical in real-world situations. This paper introduces an observer-based robust adaptive impedance control strategy that employs the (p,q)-analogue of Bernstein-Stancu operators as uncertainty estimators to tackle uncertainties in collaborative multiple electrical manipulators. This approach leverages visual task-space information and doesn’t depend on velocity feedback, enhancing cost-effectiveness and applicability in practical robotic systems. The lumped uncertainty is first modeled by this operator. The adaptation laws, derived from stability analysis, are then employed to tune its coefficients, which are not presented in the previous literature. By applying the Lyapunov lemma, the paper ensures that error signals in the controlled system are uniformly ultimately bounded (UUB). The suggested controller is evaluated in a system featuring two arms managing a rigid load. Simulation results showcase the effectiveness and versatility of the proposed approach. The outcomes are also compared with two advanced approximation techniques to demonstrate the precision and effectiveness of the suggested controller design.

Age-dependent prevalence of malocclusions requiring treatment according to the KIG classification : Amultipart cross-sectional study over a10-year period from the district of Viersen/North Rhine.

Patients with statutory health insurance (SHI) in Germany must undergo an assessment of orthodontic treatment need using the "Kieferorthopädische Indikationsgruppen" (KIG; orthodontic indication groups) classification system since 2002. Atreatment need only exists if anomalies of acertain degree of severity are present. The aim of this study was to evaluate the age-dependent prevalence and percentage distribution of KIG grades requiring treatment in patients with SHI before the age of 18over a10-year period. Between 2012 and 2021, treatment indication existed for 1951 (1025female, 926male) out of 2288 patients with SHI in the cohort of this study before the age of 18according to current SHI guidelines. The KIG classification was based on the highest existing KIG grade. There were no multiple classifications. The patient cohort was divided into three patient groups (PG) according to chronological age for analysis: PG1 < 10years of age (early treatment), PG2 10 to < 13years of age (main treatment) and PG3 13 to < 18years of age (late treatment). In PG1 (454 patients), the KIG classificationsD(26.5%), K(25.5%), M(19.4%), and P(18.0%) dominated. In PG2 (998 patients), classifications D(33.2%), predominated, whereas K(7.5%) and M(5.9%) rarely occurred. The classifications E(12.6%) and P(13.3%) appeared quite frequently. Transverse deviations occurred only about half as often in PG2 as in PG1 and PG3. In PG3 (499patients), the classification E(17.6%) was particularly common, while P(2.6%) was rare. The proportion of KIG grades 5decreased depending on age: 19% in PG1, 13.5% in PG2, 10.4% in PG3. The prevalence of sagittal classifications was highest in all age groups (45.9% in PG1, 39.1% in PG2, 31.5% in PG3). The distribution of KIG classifications requiring treatment was not homogeneous, but age dependent. The differences were particularly evident in the early treatment group and may be due to the limited applicability of the KIG classification system in patients before late mixed dentition. With increasing age at initial examination, the prevalence of sagittal classifications decreased, while that of vertical classifications increased. Still, the sagittal classifications DandM occurred most frequently in all age groups. The KIG classification Dwas always the most common in all patients until the age of18.

Detecting covolcanic ionospheric disturbances using GNSS data and a machine learning algorithm

Many natural and man-made phenomena cause ionospheric responses that can be captured in total electron content data obtained from Global Navigation Satellite System observations. However, the increasing volume of the data poses challenges for its analysis. This study provides an algorithm for detecting covolcanic ionospheric disturbances in total electron content time series based on machine learning. Using the Sarychev Peak eruption (June 11–16, 2009) as an example, we identified and labeled the observational data, proposed data features, and generated datasets. The design and number of features were chosen considering the computational efficiency of the algorithm and its potential applicability in monitoring systems. A machine learning model based on a gradient boosting technique was trained and achieved a Matthews correlation coefficient of 0.86 on the test dataset, indicating the high quality. The proposed algorithm detects 96 % of the disturbances in the datasets with a minimal number of false positives (48 out of 200 test files), exhibiting a significant improvement compared with the conventional STA\\LTA algorithm, which detected only 11 % of the disturbances. The validation of the algorithm on data corresponding to the Calbuco (2015) and Hunga Tonga-Hunga Ha’apai (2022) volcanoes eruptions showed 94 % and 81 % of the disturbances detected, respectively.

WITHDRAWN: Non-Contact Blood Pressure Monitoring from Radar Signals by a Temporal-Spatial Feature Fusion Network

Non-contact blood pressure (BP) monitoring offers a comfortable and uninterrupted means of BP assessment, free from the constraints of physical contact. This capability is particularly better for the routine surveillance of BP for individuals with hypertension, burn injuries, skin sensitivities, and other pertinent conditions. In this study, we proposed a Temporal-Spatial Feature Fusion Network (TSFN), a radar-based, non-contact approach for BP monitoring. The TSFN architecture combines Residual Networks (ResNet) for the meticulous extraction of spatial features from radar signals, Gated Recurrent Unit (GRU) for the discernment of temporal dynamics, and Multiple Head Attention (MHA) to selectively emphasize crucial information. These components collectively ensure precise BP detection from radar signals. To enhance the model's robustness, a Pseudo-Huber loss function was employed to refine the optimization process, providing a smoother gradient transition and improved stability. Evaluations demonstrated impressive accuracies, with mean errors of 0.24±6.78 mmHg for systolic blood pressure (SBP) and 0.25±5.13 mmHg for diastolic blood pressure (DBP). These outcomes meet the standards set by the British Hypertension Society (BHS) for grade ‘A’ benchmarks for SBP and DBP measurements. Notably, the TSFN model avoids the need for complex feature engineering, demonstrating its effectiveness in monitoring BP fluctuations across diverse physiological states at 2s intervals. This feature highlights its potential applicability in real-time monitoring systems, offering a promising solution for continuous, non-contact BP monitoring.

Improving vehicle warm-up performance in cold conditions using phase change materials

In cold climates, improving vehicle warm-up performance is crucial for reducing emissions and fuel consumption. Traditional methods often fail to efficiently address this issue, particularly during initial cold start periods. This study aims to enhance vehicle warm-up performance in cold conditions using phase change materials (PCMs). A thermal energy storage device was developed using paraffin wax, selected for its high energy density and a melting point of 69.3 °C, integrated with an optimized heat exchanger. Real road driving tests were conducted under urban and highway conditions, with temperatures ranging from −5 to 0 °C, using a 2.2L diesel engine vehicle. The results demonstrated a significant reduction in engine warm-up time by 20–30 %, leading to a decrease in fuel consumption and CO emissions by 365–517 g annually. The thermal energy storage device supplied up to 694.63 kJ of heat energy to the coolant, further improving vehicle efficiency and reducing environmental impact. Furthermore, evaluating PCM-based systems under real-world driving conditions is crucial for validating their practical effectiveness. Real-world variables introduce challenges that help confirm the applicability of PCM-based systems in everyday use. This approach shows potential for enhancing warm-up performance in cold climates without additional fuel consumption, outperforming conventional methods.