Point Pressure Research Articles

Automatic detection of water pipeline leakage via unsupervised learning applied on ground penetrating radar data

ABSTRACT Aging urban water supply pipelines are facing significant leakage issues. Ground penetrating radar (GPR) is an effective non-destructive method for leakage localization, but its image analysis mainly relies on interpreter experience, leading to uncertainty and inefficiency. To address this issue, an unsupervised learning method based on GPR data attributes is proposed for automatic leakage detection. First, velocity and energy attributes are extracted from B-Scan profiles to differentiate wet areas caused by leakage from normal areas. Then, the density-based spatial clustering of applications with noise (DBSCAN) clustering method is applied to automatically classify these two types of areas. The proposed method was evaluated on an experimental platform with constant water pressure and pre-drilled leakage points. Experiments show that the velocity and energy attributes decrease by 25.2 and 26.6% after leakage. Using velocity attributes yields better results, and suggestions for DBSCAN hyperparameters are provided. Feature importance analysis indicates that two-way-travel-time with a score of 46.1 and energy attribution index with a score of 32.7 significantly influence leakage identification. This method can also estimate affected leakage areas with an error not exceeding the spacing of three measurement lines, and is available across various underground conditions without needing well-labeled training data.

Waist circumference and waist-to-height ratio: Exploring the associations for high blood pressure risk in school-age adolescents

BackgroundChildhood and adolescent obesity present a global health concern, notably due to its association with adverse outcomes such as high blood pressure (HBP). Waist circumference (WC) and waist-to-height ratio (WHtR) are promising indicators for assessing HBP risk in school-age adolescents. However, their association with Stavnsbo-recommended WC cutoff points and high BP prevalence necessitates further investigation, holding potential implications for early intervention and prevention strategies. ObjectiveInvestigate the association between high blood pressure (HBP) and WC cutoff points advised by Stavnsbo, along with WHtR, in adolescents from privileged socioeconomic backgrounds in Brazil. MethodsThis cross-sectional study in 2022 involved 216 students aged 9 to 16 from a private school in Londrina, Paraná, Brazil. Data, collected through an internal project monitoring anthropometric and social indicators, included validated blood pressure assessments. Statistical analyses, employing t-tests, chi-square tests, and logistic regression, explored relationships between WC, WHtR, and blood pressure, adjusting for covariates. ResultsFemale participants had a lower prevalence of obesity (2.9% vs 5.3%) but a higher prevalence of overweight (27.5% vs 17.5%) compared to males. The overall excess weight prevalence was 26.4%, with females showing lower absolute risk scores for WC (25.5% vs 34.2%), WHtR (19.6% vs 23.7%), and HBP (21.5% vs. 40.3%). Significant associations were observed between HBP and WC (X2 = 9.759, P = 0.002) as well as WHtR (X2 = 6.335, P = 0.012) among males, with those in the “Risk” category exhibiting higher HBP prevalence. Overall, both WC and WHtR demonstrated significant associations with HBP (X2 = 12.428, P < 0.001, and X2 = 9.550, P = 0.002, respectively). Logistic regression indicated higher odds for HBP in males with risk values for WC (OR 3.876 [1.714–8.765 CI], P = 0.001) or WHtR (OR 3.684 [1.457–9.315 CI], P = 0.006). In the overall analysis, participants with risk values for WC had 3.2 (1.688–6.080 CI, P < 0.001) times higher odds, and for WHtR, 3.4 (1.679–6.934 CI, P = 0.001) times higher odds of HBP. ConclusionThis study highlights the associations between WC, WHtR, and HBP in adolescent schoolchildren. The results underscore the significance of gender-specific assessments and emphasize the potential of these anthropometric measures as valuable tools for identifying and managing HBP risk in adolescents. Further research and clinical applications are imperative to deepen understanding and address the health needs of this vulnerable population.

Pressure Distributions and Flow Regimes: An Integrated Approach For CO2 Sequestration Project Design and Evaluation in Hydraulically Fractured Shale Reservoirs

This paper introduces an integrated approach for estimating the key parameters of CO2 storage projects in depleted hydraulically fractured shale reservoirs. The approach focuses deep insights into evaluating these projects based on the pressure distribution and flow regimes. The objective is reducing the uncertainties in the design and evaluation criteria of CO2 storage by better understanding the flow mechanisms in the porous media. This understanding helps in perfectly modeling CO2 distribution and characterizing the expected flow regimes.Several analytical models are developed for the pressure behavior of CO2 multi-size scale flow mechanisms. Diffusion flow, slip flow, adsorption flow, and Darcy and non-Darcy flow are all considered. The flow in the nano-size organic particles, the micro-size kerogen particles, and the macro-size matrix is analytically described. The flow inside natural fractures in the stimulated and unstimulated reservoir volume as well as the flow of CO2 in the hydraulic fractures are modeled. Several rectangular reservoir configurations, hydraulic fracture characteristics, and petrophysical properties of the matrix, kerogen, and organic matter are examined. An estimation model for the total volume of CO2 that could be stored in the depleted fractured reservoirs is presented. The contribution of the unstimulated and stimulated porous media as well as the hydraulic fractures to the total volume is determined by characterizing the flow regimes. Different models are proposed for the time at which CO2 reaches the hydraulic fracture tips, the borders between stimulated and unstimulated porous media, and the reservoir boundary. Profound explanations are introduced for the impact of the constrained pressure on the total capacity of the reservoirs for CO2 storage.The study has reached several conclusions. The characteristics of the organic matter, kerogen, and the micro-size scale matrix do not significantly impact the pressure distribution and flow regimes. Conversely, the pressure distribution and flow regimes are significantly impacted by the characteristics of the hydraulic and natural fractures. The hydraulic fractures may offer a reasonable capacity for CO2 storage, meanwhile, the capacity of stimulated and unstimulated reservoir volume is controlled by reservoir configuration and fracture spacing. The constrained pressure may strictly reduce the total capacity of CO2 storage in the reservoir. The total volume of the injected CO2 can be determined from the pressure point when the injection pulse has reached to reservoir boundary. Beyond this point, it is not recommended to inject CO2 as it could increase reservoir pressure more than initial pressure. The pressure of the depleted reservoir before injection is a key parameter in the design and evaluation of the CO2 storage in shale reservoirs.This study introduces a novel approach for the design and evaluation criteria of CO2 storage in depleted shale reservoirs. The approach uses the pressure distribution and flow regimes that could be observed in the multi-size scale components of the reservoirs. The study proposes several models for estimating CO2 geosequestration capacity in the hydraulic fractures, stimulated and unstimulated reservoir volume as well as the total storage capacity of the reservoirs. The approach confidently addresses the concerns regarding the applicability of unconventional resources for CO2 storage and the total capacity offered by these reservoirs for this purpose.

Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features

Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.

Climate change and health vulnerability assessments to increase regional climate resilience

Abstract Background The impacts of climate change on health and healthcare systems exhibit complex interrelationships, varying across time and space. Population groups already disadvantaged due to their demographics, health, socioeconomic status, or geographical location tend to be particularly vulnerable to climate-related health risks. Our work aims to develop a method for a systematic quantitative vulnerability assessment to make regional vulnerabilities comparatively measurable. Methods Building on the WHO framework on Climate Change and Health Vulnerability and Adaptation Assessment (2021) a data mapping process was carried out to identify and validate vulnerability factors and quantitative indicators on a small-scale spatial resolution for the five dimensions: demographic, socioeconomic, biological/health, sociopolitical, and geographic/climate. Regional vulnerability scores for each indicator were calculated and visualized as geographic maps and circular barplots to allow for easily understandable interpretation. Results A set of 25 quantitative vulnerability indicators from a multitude of data sources - combining register data, survey data, and geospatial data - was compiled. Main challenges were the identification of sociopolitical vulnerability factors suitable for Austria and data availability on a small-scale level. The indicator set was used to identify regions that are particularly vulnerable due to their population composition, disease burden, and geographical location (e.g. regions in Eastern Austria with high shares of persons &amp;lt; 65 years of age, unemployment, and heat days). Conclusions Systematically assessing vulnerabilities in key dimensions is central to identifying regionally specific pressure points, allowing for a quantitative baseline to derive adequate and tailored climate change adaptation measures to sustain future health. Systematic assessment provides essential insights for policymakers, public health officials, and disaster management agencies.

Synthesis of novel green nanocomposites by considering enhanced oil recovery and asphaltene adsorption effectiveness in calcite and dolomite formations

Asphaltene deposition is known as the main issues in reservoirs, which causes formation damage in reservoir. This research investigated the effects of a newly introduced green nanocomposites, namely eucalyptus/CuO/Fe3O4/xanthan nanocomposites (EC-NCs), on the inhibition of asphaltene precipitation in calcite and dolomite formations. First, at two time steps of 48 and 96 h, asphaltene adsorption on EC-NCs surfaces was examined through scanning electron microscope (SEM) tests in both calcite and formation tests. Moreover, asphaltene adsorption on the surface of EC-NCs was confirmed through isotherm models including Langmuir and Freundlich and the interfacial tension between CO2 and oil. Finally, a series of static and dynamic tests were performed at the highest adsorption pressure points, including 4200, 3950, and 3700 psi, and at the highest adsorption time step, i.e., 96 h, and the effects of asphaltene adsorption were seen on permeability reduction and asphaltene precipitation. EC-NCs exhibited superior efficacy in both asphaltene adsorption and precipitation inhibition. Two separate pressure changes were observed, and the ratios of the interfacial tension slope in the 2nd to the 1st regions [1st: 200–2700 psi, 2nd:2700–4200 psi] were changed from 19.44 % to 25.00 % and 29.74 % in 48 and 96 h, respectively, and this confirms that asphaltene had indeed adhered to the surface of the EC-NCs. Moreover, at 48, and 96 h, the size reductions of particles in SEM for calcite and dolomite were (29.27 %, 56.81 %), and (59.09 %, 62.25 %), respectively, which shows asphaltene adsorption was more efficient in dolomite formation, and in 96 h.

Associations between environmental factors and running performance: An observational study of the Berlin Marathon.

Extensive research has delved into the impact of environmental circumstances on the pacing and performance of professional marathon runners. However, the effects of environmental conditions on the pacing strategies employed by marathon participants in general remain relatively unexplored. This study aimed to examine the potential associations between various environmental factors, encompassing temperature, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and the pacing behavior of men and women. The retrospective analysis involved a comprehensive dataset comprising records from a total of 668,509 runners (520,521 men and 147,988 women) who participated in the 'Berlin Marathon' events between the years 1999 and 2019. Through correlations, Ordinary Least Squares (OLS) regression, and machine learning (ML) methods, we investigated the relationships between adjusted average temperature values, barometric pressure, humidity, precipitation, sunshine, cloud cover, wind speed, and dew point, and their impact on race times and paces. This analysis was conducted across distinct performance groups, segmented by 30-minute intervals, for race durations between 2 hours and 30 minutes to 6 hours. The results revealed a noteworthy negative correlation between rising temperatures and declining humidity throughout the day and the running speed of marathon participants in the 'Berlin Marathon.' This effect was more pronounced among men than women. The average pace for the full race showed positive correlations with temperature and minutes of sunshine for both men and women. However, it is important to note that the predictive capacity of our model, utilizing weather variables as predictors, was limited, accounting for only 10% of the variance in race pace. The susceptibility to temperature and humidity fluctuations exhibited a discernible increase as the marathon progressed. While weather conditions exerted discernible influences on running speeds and outcomes, they did not emerge as significant predictors of pacing.

Neural Impedance Boundary (NeIB): a neural-network based framework for acoustic surface impedance estimation utilizing sparse measurement data

Amidst recent advancements in the 3D digital representation that have significantly enhanced the modeling of geometric attributes of pre-existing environments, accurate estimation of acoustic boundary conditions remains a complex challenge. This paper presents a novel way to determine what we refer to as a neural boundary field, using physics-informed neural networks (PINN). The aim is to estimate the surface impedance measured in-situ by utilizing several points of sound field pressure. This approach couples the Helmholtz equation with automatic differentiation in the PINN framework to estimate accurately the surface impedance using a hybrid modeling approach where measurement data and domain knowledge in the form of equations for the acoustic waves are combined. As a proof-of-concept, we train the neural networks using 2D sound field data obtained from high-fidelity numerical acoustical simulations that incorporate actual surface materials parameters. We discuss the measurement techniques associated with this method and outline our vision for its application to 3D scene reconstructions in the future.

Lesion Size Location Dependency on Maximum Pressure in Osteochondral Defects: Experiments and Finite-Element Analysis.

Osteochondral defects (OCDs) in the knee joint have significant clinical implications, particularly regarding contact pressures and pressure distribution. Understanding how these factors are influenced by defect size and location is crucial for developing effective therapeutic strategies. The purpose of this study was to investigate the impact of defect size and location on contact pressures and pressure distribution in the knee joint. It was hypothesized that an increase in defect size would result in elevated contact pressures and alterations in pressure distribution, with specific variations related to defect location. Descriptive laboratory study. The study utilized 6 cadaveric knees, including the patella and fibula, subjected to controlled compressive loading for measuring contact pressures. Simultaneously, computed tomography-based models were created for finite-element analysis (FEA) to investigate the impact of varying defect sizes and locations on contact pressures and pressure distribution in the knee joint, excluding the patellofemoral joint. The study employed analysis of variance to assess contact pressure and defect size association. Comparison between medial and lateral femoral condyles at full extension and 30° flexion angle was performed, followed by post hoc testing. Fisher exact test analyzed peak pressure point location and defect size, categorizing them into medial and lateral. An increase in defect size corresponded with heightened contact pressures on both medial and lateral femoral condyles at full extension (P = .013 for medial and P = .024 for lateral). However, this correlation did not yield significant differences at 30° of flexion (P = .674 for medial and P = .333 for lateral). During mechanical testing, the highest pressures occurred near 5 mm defect dimensions. FEAs showed a significant increase in pressure and circumferential-edge stress with 7-mm defects. Peak contact pressure points shifted laterally with more significant defects. Our study demonstrated the impact of defect size, location, and alignment on knee joint contact pressures. Intervening promptly with defects exceeding 3 mm is crucial, as significant stress levels manifest beyond this threshold. Significant increases in contact pressures were noted with larger defect sizes, particularly between 3 and 10 mm at full extension. Peak pressure points shifted with defect size increments, and alignment variations showed minimal stress variation at 30° compared with 0°. FEA validated increasing contact pressures up to 7 mm defect size, beyond which pressures stabilized or slightly decreased. A concentrated pressure distribution on the medial side was observed. These findings inform our understanding of the biomechanical implications of OCDs. In the field of sports medicine, this research offers valuable insights to clinicians and researchers, elucidating key factors influencing knee joint health and the potential consequences of OCDs.

Three-dimensional physical experimental study on mechanisms and influencing factors of CO2 huff-n-puff and flooding process in shale reservoirs after fracturing

Carbon dioxide (CO2) capture and geological storage technology is a promising strategy for enhancing oil and gas production and mitigating climate change in unconventional formations. In shale reservoirs, production decline is rapid under volumetric fracturing development. Currently, CO2 injection and huff-n-puff techniques are cost-effective methods to boost shale oil recovery. However, traditional huff-n-puff techniques are limited in capturing reservoir heterogeneity and intricate fracture conditions. This study employed a three-dimensional physical experimental apparatus to explore CO₂ huff-n-puff dynamics across six distinct fracture patterns. By implementing a real-time monitoring system with multiple pressure points, the study elucidated the impacts of varying fracture penetration levels and spacing on dynamic pressure wave patterns, mobilization extent, and development outcomes during CO2 huff-n-puff. The results demonstrate that fractures substantially improve CO₂ huff-n-puff efficiency in shale reservoirs compared to a seamless model, with Model 6 achieving the highest recovery rate of 42.38%. As fracture penetration increases, CO₂ dynamic coverage expands. However, full penetration leads to preferential flow through fractures, reducing matrix contact and diminishing huff-n-puff efficiency. Reduced fracture spacing enlarges the well's control domain, amplifies CO2 huff-n-puff dynamic coverage area, and increases oil production and recovery rate. Conversely, decreasing fracture spacing accelerates oil and gas displacement rates, leading to higher economic expenses. The study concludes that 2/3 fracture penetration in five fractures offers the most efficient recovery method. For a 500 m horizontal well, optimal fracture spacing is recommended at 125 m, with a fracture length of 145.8 m for a well spacing of 417 m.

Beyond rheumatoid nodules in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease predominantly affecting synovial joints. Extra-articular manifestations, including skin involvement, can also occur. The most frequent cutaneous manifestation in RA patients is rheumatoid nodules, occurring in 20-30% of seropositive individuals. These nodules are typically firm, painless, and located on pressure points such as the hands and elbows. However, in a minority of cases, other skin conditions can complicate RA, notably palisaded neutrophilic granulomatous dermatitis (PNGD). PNGD presents as erythematous papules or plaques, often pruritic and distributed symmetrically over extensor surfaces, making it challenging to differentiate from rheumatoid nodules. Histopathological examination is crucial to establish the diagnosis. High clinical suspicion and appropriate referral to Dermatology are essential for accurate diagnosis and management. Treatment of PNGD is focused on underlying disease control. Other options include topical, intralesional or systemic corticosteroids, dapsone or hydroxychloroquine. Herein, we present the case of a 71-year-old woman with RA who developed PNGD, highlighting the importance of a multidisciplinary approach for achieving a favorable clinical outcome.

Invasion Characteristics of Marginal Water under the Control of High-Permeability Zones and Its Influence on the Development of Vertical Heterogeneous Gas Reservoirs

In-depth understanding of the gas–water seepage law caused by different degrees of gas layer perforation and varying gas production rates is key to determining a reasonable development technology policy for vertical heterogeneous edge-water gas reservoirs. Based on core physical data from the entire section of the X2 well, a large-scale high-pressure positive-rhythm profile model that takes into account the influence of “discontinuous interlayer” was innovatively established. The water intrusion process of the gas layer profile under different gas production rates and degrees of gas layer perforation was simulated using an electrical resistivity scanning device. The experimental model has an area of 3000 cm2, with a maximum pressure of 70 MPa and a maximum temperature resistance of 150 °C. It includes 456 evenly distributed fluid saturation test points to accurately monitor the gas–water distribution, addressing the issues of small bearing pressure and insufficient saturation monitoring points found in other large-scale models. The experimental results show that, in heterogeneous reservoirs, the high-permeability zone controls the invasion path of edge water, which is the main reason for the uneven invasion of edge water. For the positive-rhythm profile of the F layer, a higher gas production rate (1000 mL/min) shortens the water-free gas recovery period of the gas well and reduces the recovery rate. Perforating the upper two-thirds of the layer can inhibit edge-water breakthrough, prolong the water-free gas recovery period of the gas well, enable the gas–water interface to advance more uniformly, and enhance the recovery degree. The results of this study greatly enhance our understanding of the water invasion characteristics of positive-rhythm reservoirs under the influence of different gas production rates and varying degrees of gas layer perforation.

Examining the Optimization of Spray Cleaning Performance for LiDAR Sensor

Pollutants degrade the performance of LiDAR sensors used in autonomous vehicles. Therefore, there is an urgent need to develop cleaning technology for these sensors. In this study, a solid-state LiDAR sensor was selected as a target and sprayed/dried with 2.5 g of a mixture of Arizona dust and Kaolin. To achieve optimal LiDAR cleaning performance, the washer pressure, spray time, spray angle, and target point were selected as major variables. Additionally, an optimal cleaning solution for each spray was formed via the design of experiments and optimization techniques. Model suitability was observed for the second spray through to the fourth. The cleaning rate increased with the washer pressure and spray time. The influence of these variables decreased as the number of sprays increased. The spray angle and target point exhibited no significant influence, but excellent cleaning was observed in some central areas. Verification test results were within 3% for the second through fourth sprays, indicating reliability. This study used a designed experiment with 30 scenarios to reveal optimized conditions for protecting the sensor performance from external visibility obstructions. Disclosing the optimization method lowers the barrier for sensor cleaning manufacturers to develop their own technology, which ultimately enhances safer and more efficient autonomous driving.

“I Felt Like There Was Something Wrong in My Brain”: Growing Up with Trauma – How Young People Conceptualise, Self-Manage and Seek Help for Mental Health Problems

Abstract Background Youth mental health is an important global healthcare topic and early interventions that are timely and evidence-based to support young people can increase quality of life and lower deaths by suicide. Research exploring young people’s mental health experiences and how they manage can further understanding into help-seeking processes. Objective This study aimed to explore young people’s experiences of living with and managing mental health problems and how this impacts professional help-seeking. Methods Eighteen young people were recruited, aged 16–25 years, with experiences of help-seeking to services for mental health problems (N = 18). Data were analysed using Constructivist Grounded Theory methods (Charmaz, Constructing grounded theory, 2014). Findings The findings were presented across three sub-categories: (1) “Early experiences”; (2) “Conceptualising mental health” and (3) “Managing mental health”. Findings expand understanding on the resource pressures that young people experience whilst managing persistent mental distress emanating from early experiences of trauma, life stressors, and developmental tasks. Findings also report lived experiences of pain, loneliness and stigma, and how individual conceptualisations of mental health are informed. The preference for self-reliance can be rooted in developmental needs or learned behaviours and how this impacts self-management and help seeking is discussed. Conclusion Through an enhanced understanding about how young people experience mental distress, developmental pressure points, marginalisation and stigma, mental health providers can prioritise individualised approaches to healthcare that can both respect a young person’s individual conceptualizations and positively leverage self-management strategies, which can contribute positively to young people’s development, quality of life, and healthcare outcomes.

11. Cervicogenic headache and occipital neuralgia.

Cervicogenic headache (CEH) and occipital neuralgia (ON) are headaches originating in the occiput and that radiate to the vertex. Because of the intimate relationship between structures based in the occiput and those in the upper cervical region, there is significant overlap between the presentation of CEH and ON. Diagnosis starts with a headache history to assess for diagnostic criteria formulated by the International Headache Society. Physical examination evaluates range of motion of the neck and the presence of tender areas or pressure points. The literature for the diagnosis and treatment of CEH and ON was searched from 2015 through August 2022, retrieved, and summarized. Conservative treatment includes pain education and self-care, analgesic medication, physical therapy (such as reducing secondary muscle tension and improving posture), the use of TENS (transcutaneous electrical nerve stimulation), or a combination of the aforementioned treatments. Injection at various anatomical locations with local anesthetic with or without corticosteroids can provide pain relief for a short period. Deep cervical plexus block can result in improved pain for less than 6 months. In both CEH and ON, an occipital nerve block can provide important diagnostic information and improve pain in some patients, with PRF providing greater long-term pain control. Radiofrequency ablation of the cervical facet joints can result in improvement for over 1 year. Occipital nerve stimulation (ONS) should be considered for the treatment of refractory ON. The treatment of CEH preferentially consists of radiofrequency treatment of the facet joints, while for ON, pulsed radiofrequency of the occipital nerves is indicated. For refractory cases, ONS may be considered.

Under pressure – Association of the arm position and leading circulatory structure behind the pressure point in cardiopulmonary resuscitation patients

BackgroundThoracic computed tomography scans (CT) are used by several study groups to investigate the circulatory structures (heart and vessels) located behind the pressure point for chest compressions. Yet, it remains unclear how the positioning of these structures is influenced by factors such as intubation, the respiratory cycle and arm positioning. MethodsWe retrospectively analyzed data of adult patients with in- or out-of-hospital cardiac arrest who underwent thoracic CT imaging within one year before or up to six months after arrest. A region of interest (ROI) behind the pressure point was defined. The largest structure within this region was defined as “leading circulatory structure”, which was the primary outcome. Airway status (intubated versus spontaneous breathing), respiratory cycle (inspiration, expiration, resting expiratory position), and arm position (up over the head versus down beside the trunk) served as covariates in an ordinal regression model. ResultsAmong 500 initially screened patients, 411 (82.2 %) were included in the analysis. There was a significant association between the arm position and the leading circulatory structure behind the pressure point. However, no association was found with airway status or respiratory cycle. The most frequently identified leading circulatory structure was the left atrium (arms up: 41.8 %, down: 50.7 %), followed by the ascending aorta (up: 23.8 % vs. down: 16.7 %). The left ventricle was the leading structure in only one case (0.2 %, arms down). ConclusionThis study shows that arm position is significantly associated with the leading circulatory structure behind the pressure point for chest compressions in cardiac arrest.

Molecular simulation to understand the effect of methane concentration and moisture contents on hydrogen adsorption in kerogens

The adsorption behaviors of methane(CH4), hydrogen(H2), and their mixtures are crucial for estimating the underground hydrogen storage(UHS) capacity in depleted shale gas reservoirs. In this study, we developed four formulated kerogen models, each representing different maturation levels of type II kerogen molecules via utilizing molecular dynamics (MD) methodologies. We investigated the adsorption characteristics of pure H2 and mixed gases(H2 and CH4) on these kerogen models with varying gas components(0.5:0.5 to 0.9:0.1) and moisture content(0–3.0 wt percentage) through grand canonical Monte Carlo(GCMC) simulations. Our findings reveal that pure H2 lacks a pressure point for adsorption equilibrium below 100 MPa at ambient temperature(298K). The competitive adsorption dynamics between H2 and CH4 on kerogen models demonstrate that CH4 exhibits stronger selectivity at lower pressures. Yet, this selectivity diminishes compared to H2 as system pressure increases, indicating turning points in pressure buildup with kerogen maturity. Additionally, the presence of pre-loading H2O molecules reduces the adsorption capacity of mixed gases, particularly affecting CH4 more than H2. This study offers profound insights into the effect of kerogen maturity and moisture content on the interaction between H2/CH4 and kerogen at a microscopic scale.

Positioning in pelvic robotic surgery and its repercussions on patient safety

Objectives: To describe the positions used in robotic pelvic surgery, observe the impact of these positions on patient safety and evaluate precautions for the patient's surgical positioning during the intraoperative period. Methods: A Systematic Literature Review (RSL) was carried out over the last 5 years. Results: 184 articles were located, of which 13 were selected according to PRISMA recommendations. Result: It was found that the main type of positioning used in robotic surgeries of the pelvic region is the trendelenburg associated with the lithotomy position, in addition to the pneumopritoneus. The main complications are: bradycardia, thromboembolisms, increased ocular, intra-abdominal and intra-thoracic pressure, neuropraxia, compartment syndrome, increased ICP, facial edema, injuries to pressure points. Prevention measures include the use of special anti-skid padding and mattresses, buttock padding, adequate positioning of the limbs. The use of fastening straps is recommended to avoid the risk of slips and falls and repositioning to the supine position for 10 minutes every 3 hours.

Model for the Failure Prediction Mechanism of In-Service Pipelines Based on IoT Technology

With the rapid increase in pipeline mileage in China, the accurate prediction of corrosion issues in in-service pipelines has become crucial for ensuring safe pipeline operation. Traditional pipeline leakage monitoring methods are significantly limited by human factors and equipment precision, making it challenging to predict and identify leakage points accurately. Therefore, aligned with the trend of intelligent pipeline development, this study aims to construct a failure pressure prediction mechanism model for corroded pipelines based on IoT technology. This model leverages intelligent sensing and prediction to assess the safety status of corroded pipeline sections. Ultrasonic phased array technology detects specific corrosion points and detailed defect parameters within pipeline sections. The parameters are then utilized in the Simdroid domestic finite element analysis model to simulate the ultimate burst pressure of the pipeline. A single-variable approach is employed to analyze the sensitivity of different parameters to the pipeline’s ultimate burst pressure, with the minimum burst pressure point of multi-point corroded sections selected as the overall segment failure pressure. Finite element simulation data are integrated into a neural network database to predict the pipeline failure pressure. The real-time operational data of the pipeline are monitored using negative-pressure wave sensing. The operational pressure of the corroded points is compared with the algorithm-predicted failure pressure; if the values approach a critical threshold, an alarm is triggered. Moreover, the remote control terminals evaluate the pipeline’s self-rescue time, providing a buffer for pipeline leakage self-rescue. The failure prediction mechanism model for in-service pipelines was applied to the Fujian–Guangdong branch of the West–East Gas Pipeline III to verify its accuracy and feasibility. The research results offer technical support for the maintenance and emergency repair of pipeline leakage scenarios, leveraging intelligent pipeline technology to reduce costs and increase the efficiency of pipeline operations, thereby supporting the sustainable development of China’s oil and gas pipelines with theoretical and technical backing.

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.