Pressure Research Articles

Rainfall Modeling using a Machine Learning Approach as Support for the Early Warning System for Flood Disasters in Malang Regency, Indonesia

Malang is one of the areas in East Java - Indonesia that has the potential to experience flooding caused by high rainfall. Floods have a negative impact on both agriculture and public health. Therefore, it is necessary to create a rainfall prediction model in Malang that supports the creation of an early warning system for flood disasters. In this research, a machine learning approach was used, namely the Long Short Term Memory model, to model monthly rainfall at three stations in Malang Regency, namely Abd. Saleh Airbase Station, Karangkates Station and Karangploso Station. Several inputs were tried, namely rainfall lag from the station itself and from neighboring stations as well as other weather variables, namely air humidity, air pressure and air temperature. The results obtained from this research are the best LSTM model for Abd. Saleh Airbase Station rainfall in is similar to the best LSTM model for Karangploso Station rainfall, namely a LSTM model with input rainfall lag 6 for the station itself. Meanwhile, the best model for Karangkates Station rainfall is a model with input rainfall lag 1, lag 4 and lag 6 from the three stations.. KEYWORDS :Machine learning, Long short term memory, Monthly rainfall, Modeling.

A decoupling control of air supply for the PEM fuel cell with slide mode-active disturbance rejection controller

The air supply subsystem is crucial for optimizing proton exchange membrane fuel cells (PEMFCs). This study develops a transient model for the air subsystem and proposes a control strategy that decouples air pressure and flow using sliding mode-active disturbance rejection control (SM-ADRC). In this study, adjustments to the air compressor speed and the opening of the back-pressure valve are utilized to achieve this control strategy. The sliding mode-extended state observer (SM-ESO) is employed to estimate and compensate for uncertainties within the system, while the sliding mode-nonlinear state error feedback (SM-NLSEF) is used to simultaneously control air flow and pressure. The study evaluates the performance of the SM-ADRC controller across various scenarios and environmental pressures. Evaluations show that the SM-ADRC controller significantly outperforms traditional methods like Fuzzy-ADRC, ADRC, SMC, and PID, with overshoot reductions of 18.2%, 34.1%, 42.5%, and 63.7%, respectively, and adjustment time under 1 s. Additionally, SM-ADRC achieves a 4.1% and 1.8% improvement in efficiency compared to PID and ADRC.

Experimental Analysis and Cost Assessment of a Novel Variable-Volume Air Storage Device Designed for Compressed Air Energy Storage

One significant reason limiting the widespread application of compressed air energy storage is the high cost of ground-level air storage devices. Previous work by the authors’ team proposed a novel variable-volume air storage (VVAS) device based on working fluid phase change, effectively increasing the air storage capacity. However, the early study only focused on thermodynamic performance analysis. Therefore, this study established an experimental platform for the VVAS device to test its feasibility and developed a cost assessment model to analyze its economic performance. The results demonstrate that the VVAS device allows the air in the storage tank to continue near-isobaric release under the displacement of gaseous R134a at the other side of the bladder when the air pressure in the storage tank decreases to equal the air pressure at the throttle valve outlet. The effective air storage density of the VVAS device can be increased by over 50% under the different air storage pressures. The efficient utilization of the air storage device can significantly reduce its volume and lower storage costs. The cost analysis indicates that the unit air storage cost for the VVAS device is 50.20 $/kg, which is significantly lower than the conventional device (80.34 $/kg).

A case of mimicking normal pressure hydrocephalus in a female older patient: co-existence of probable Alzheimer’s dementia and colpocephaly

ABSTRACT Colpocephaly, a cause of hydrocephalus, is characterized by disproportionately enlarged occipital horns of the lateral ventricles. It is rarely seen in the older adults. Few cases were reported as colpocephaly in elderly individuals in the literature. However, it has not been reported as idiopathic normal pressure hydrocephalus-like clinical presentation in older adults. To the best of our knowledge, the co-existence of colpocephaly and Alzheimer’s disease has not been reported previously. In these respects, it is the first case in the literature.

Similarity of scaled-down tests of water entry slamming considering the effects of atmospheric pressure and density

The water entry cavity and load characteristics obtained through scaled-down tests are correlated with the atmospheric pressure and density at the free surface. The evaluation of the influence of the cavitation number and atmospheric density coefficient is highly essential for scale tests to improve the prototype prediction accuracy. Focusing on the similarity criterion simulations and load prediction of the scaled-down tests, this study conducts the simulation tests of the water entry characteristics of the scaled-down model under different environments: normal pressure, reduced pressure, and reduced pressure and heavy gas replacement. Moreover, the influence of the cavitation number and atmospheric density coefficient on the multiphase flow, slamming load, and air cushion effect is discussed. The “air cushion effect” is formed at the top of the vehicle during water entry process, which affects the peak narrow pulse width slamming load. Furthermore, the “air cushion” experiences expansion–stability–rupture–escape with increasing water invasion depth. As the atmospheric pressure decreases, the gas tends to thin and the retention inertia weakens. The decrease in the “air cushion” buffering capacity leads to the increase in the slamming load and the expansion of the cavity scale formed by the liquid. Excessive simulation of the dynamic pressure results in the delayed closure of the cavity surface and the slow fall of the water curtain. As the atmospheric density increases, the retention inertia of gas increases because of the increase in the molecular mass, the slamming load gradually decreases, and the closure time of the cavitation and water curtain decreases. The research results of this paper provide some reference for the similarity transformation of the scaled-down test and the pre-research of the prototype.

Experimental study on aeration characteristics of plunging wave impact on a vertical cylinder

The impact force exerted by plunging breakers on structures is most pronounced, with the impact pressure being most sensitive to the changes in air entrainment, posing a threat to the structural safety of offshore wind turbines. However, the current explanations of the high impact force mechanisms in plunging waves are relatively superficial, and the relationship between the air entrainment motion and pressure oscillations remains unclear. According to the experimentally obtained pressure characteristics and the formation of entrapped air, this paper systematically divides the process of plunging wave impact on a vertical cylinder into three stages: breaking wave crest impact, entrained air cavity impact, and plume impact. Employing a bubble image velocimetry platform to visualize flow field characteristics and quantify turbulence intensity, we investigated the connection between gas motion and impact loads. The results indicate that the conversion of gas energy is closely related to the load characteristics, with the maximum impact force occurring near the peak of the energy gradient. The gas movement significantly influences pressure oscillations, which are positively correlated with the gas's physical compression and expansion characteristics. This study has uncovered new insights into the phenomenon of plunging waves impacting wind turbine towers and has enhanced the understanding of its load characteristics.

Rationale and Design of the PE-TRACT Trial - A Multicenter Randomized Trial to Evaluate Catheter-Directed Therapy for the Treatment of Intermediate-Risk Pulmonary Embolism

The optimal management of patients with intermediate-risk pulmonary embolism (PE), who have right heart dysfunction (determined by a combination of imaging and cardiac biomarkers) but a normal blood pressure, is uncertain. These patients suffer from reduced functional capacity and a lower quality of life over the long-term, despite use of anticoagulant therapy. Catheter-directed therapy (CDT) is a promising treatment for acute PE that rapidly removes thrombus and potentially improves cardiac dysfunction. However, CDT has risk and is costly, and it is not known whether it improves long-term cardiorespiratory fitness and/or quality of life compared with anticoagulation alone. We are therefore conducting an open-label, assessor-blinded, multicenter randomized trial, the Pulmonary Embolism: Thrombus Removal with Catheter-Directed Therapy (PE-TRACT) Study, to compare CDT plus anticoagulation (CDT group) with anticoagulation alone (No-CDT group) in 500 patients with intermediate-risk PE. The primary study hypothesis is that CDT will increase the peak oxygen uptake (peak VO2) with cardiopulmonary exercise testing at 3 months and reduce New York Heart Association (NYHA) Class at 12 months compared with No-CDT. These two primary efficacy outcomes will be analyzed sequentially using a "gatekeeping" procedure; for NYHA class to be compared, peak oxygen consumption must first be shown to be significantly increased by CDT. Safety and cost-effectiveness will also be assessed. When completed, PE-TRACT will provide important evidence regarding the benefits and risks of CDT to treat intermediate-risk PE compared with anticoagulation alone.

An optical investigation of the effects of diesel injection pressure and intake air temperature in an ammonia-diesel dual-fuel engine under low-load conditions

An optical investigation of the effects of diesel injection pressure and intake air temperature in an ammonia-diesel dual-fuel engine under low-load conditions

Design and development of wire-mesh packed bed liquid desiccant dehumidifier to reduce pressure-drop and carryover

In response to the high air-pressure drop and desiccant carryover experienced in liquid desiccant-packed bed dehumidifiers, which adversely affect the room environment and system economics, this study explored various modified wire-mesh packed bed designs. It experimentally evaluated dehumidification efficiency, and thermal performance across different corrugated wire-mesh packings, introducing a new design for cost-effective moisture removal while reducing pressure drop and desiccant carryover. Three specially designed packings (P-1, P-2, and P-3) were tested against standard commercial options like Celdek (7090, 6090, 5090) and Mellapak 250Y, focusing on optimizing performance. At a constant optimal concentration of the desiccant solution, the effects of airflow rates (ma), solution flow rates (ms), and solution temperatures (Ts) on the performance of the system was analyzed. The findings indicated that P-1 performed better than others, particularly in terms of condensation rate (CR) and moisture effectiveness (εm). Specifically, it outperformed the acrylonitrile butadiene styrene (ABS) (benchmark) packing by 22.1 % and 8 % respectively at a solution concentration of 40 wt% and ma of 0.025 kg/s under ambient conditions of nearly 0.02921 kg/kg specific humidity and 31 °C temperature. Increasing the void fraction led to a reduction in air pressure drop while enhancing the wettability, and dehumidification efficiency. The packing, P-1, made of stainless steel (SS340) with 0.3 cm mesh was finally proposed to obtain better performance for dehumidification applications in buildings. Additionally, its potential to reduce air-pressure drop and desiccant carry-over can provide improved energy efficiency and enhanced air quality, making it a practical choice for sustainable building cooling solutions.

Design, fabrication, and testing of a new soft-pouch robot with 6 degrees of freedom to expand the reach of open and endonasal skull base approaches

OBJECTIVE Most robots currently used in neurosurgery aid surgeons in placing spinal hardware and guiding electrodes and biopsy probes toward brain targets. These robots are inflexible, cannot turn corners, and exert excessive force when dissecting and retracting brain tissue, limiting their applicability in cranial base surgery. In this study, the authors present a novel soft-pouch robot prototype driven by compressed air and capable of gentle tissue manipulation. The robot is manufactured with technology developed by the authors, with multiple bidirectional bending points and a miniature camera running through the robot’s central channel. METHODS A soft, pneumatically driven pouch manipulator was created using a novel rapid and scalable system (integrated multilayer pouch robots with inkjet-patterned thin films). Made from 4 layers of thin, low-density polyethylene films, the manipulator has a thin deflated profile (152 µm) and contains 5 independent bidirectional joints with 50° range in each direction, as well as a wrapping end-effector. The robot carries a camera through its central channel. Four cadaveric models were used to demonstrate the robotic prototype being maneuvered inside different anatomical structures during simulated endonasal and posterior fossa approaches, with a manually positioned robot base and manually controlled air pressures. RESULTS The robot is a pneumatically driven, soft-continuum manipulator with 12 control inputs and 6 independently controllable degrees of freedom. This design enables in-plane obstacle avoidance and orientation control. The robot is trapezoidal-shaped, with a total weight of 0.4 g, a 10-mm-wide distal end, and a length of 138 mm. The variable production cost (materials cost) of the manipulator is approximately $1. The manipulator is maneuvered to enter the maxillary sinus and through the endonasal corridor, demonstrating its potential use for anterior skull base approaches. It is also successfully maneuvered around the pons in a simulated retrosigmoid approach. CONCLUSIONS This robot offers a promising solution for safely maneuvering through narrow surgical windows encountered during skull base approaches. The multiple bending points of the robot, combined with its passive deformation capacity, allow it to turn around immovable structures, expanding the reach of surgical openings. The cost-effectiveness, rapid production, and scalability of the robot represent additional advantages.

Effect of non-static pre-oxidation treatment on the cyclic ablation behavior of SiC-ZrC/SiC multilayer coated graphite

SiC-ZrC based ceramics have caught much attention as coatings for graphite materials. However, the coefficient of thermal expansion (CTE) mismatch between coatings and matrix severely limits their application in harsh environments. Herein, we proposed a non-static pre-oxidation treatment under an air pressure of 0.05 MPa to increase the surface roughness of graphite matrix to 18.054 μm and then prepared the SiC-ZrC/SiC multilayer coating on the pre-oxidized graphite. The results demonstrate that a firm interlocking structure was formed between SiC-ZrC/SiC multilayer coating and graphite matrix, which effectively improved the bonding strength and enhanced the cyclic ablation resistance of coatings. The bonding strength of SiC-ZrC/SiC multilayer coated on graphite pre-oxidized under negative pressure was 5.70±0.42 MPa, which was 54.89% higher than that on graphite pre-oxidized under atmospheric pressure (3.68±0.08 MPa). After the plasma ablation for 360 s (60 s × 6), the linear and mass ablation rates of SiC-ZrC/SiC multilayer coated graphite which was pre-oxidized under negative pressure were -0.083 μm/s and 0.101 mg/s, respectively.

Design and analysis of pneumatic composite phononic crystal

Tuning band gaps in soft phononic crystal by air pressure-induced deformation is a feasible method to manipulate elastic waves. In this paper, we design a pneumatic composite phononic crystal that incorporates a labyrinthine structure into the Helmholtz-type phononic crystal. By calculating the band gaps of the representative volume element (RVE) arranged in a two-dimensional triangular lattice, we find that this design effectively extends the transmission path of sound waves, achieving band gaps in the vicinity of the lower frequency range. We apply pressure to the scatterer’s pneumatic actuator to adjust its volume through the deformation of soft material, altering the air filling rate and enabling reversible adjustment of the frequency and width of band gaps. Nonlinear finite element simulations investigate the effects of different pressure levels on the band gaps. Furthermore, we explore bandgaps tuning in one-dimensional phononic crystals and the impact of various types of point defects on the transmission characteristics of phononic crystals. Results show that the pneumatic phononic crystal exhibits rich band gaps under air pressure loading, in which a low-frequency band gap exists, and is capable of reversible continuous tuning. This study provides valuable for manipulating elastic waves in periodic structures and designing soft acoustic devices.

Development of an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle.

To enhance meteorological detection methods, an atmospheric boundary layer detection system based on a rotary-wing unmanned aerial vehicle (UAV) was proposed. Computational fluid dynamics (CFD) was employed to model the surrounding airflow distribution during UAV hovering, thereby determining the optimal positions for sensor installation. A novel radiation shield was designed for the temperature sensor, offering both excellent radiation shielding and superior ventilation. To further improve temperature measurement accuracy, an error correction model based on CFD and neural network algorithms was designed. CFD was used to quantify the temperature measurement errors of the sensor under different environmental conditions. Subsequently, random forest and multilayer perceptron algorithms were employed to train and learn from the simulated temperature errors, resulting in the development of the error correction model. To validate the accuracy of the detection system, comparative experiments were conducted using the measurement values from the 076B temperature observation instrument as a reference. The experimental results indicate that the mean absolute error, root mean square error, and correlation coefficient between the experimental temperature errors and the algorithm-predicted errors are 0.055, 0.066, and 0.971 °C, respectively. The average error of the corrected temperature data is 0.05 °C, which shows substantial agreement with the reference temperature data. During UAV hovering, the average discrepancies between the temperature, humidity, and air pressure data of the detection system and the ground-based reference data are 0.6 °C, 1.6% RH, and 0.77 hPa, respectively.

A General Neurologist's Practical Diagnostic Algorithm for Atypical Parkinsonian Disorders: A Consensus Statement.

The most common four neurodegenerative atypical parkinsonian disorders (APDs) are progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal syndrome (CBS), and dementia with Lewy bodies (DLB). Their formal diagnostic criteria often require subspecialty experience to implement as designed and all require excluding competing diagnoses without clearly specifying how to do that. Validated diagnostic criteria are not available at all for many of the other common APDs, including normal pressure hydrocephalus (NPH), vascular parkinsonism (VP), or drug-induced parkinsonism (DIP). APDs also include conditions of structural, genetic, vascular, toxic/metabolic, infectious, and autoimmune origin. Their differential diagnosis can be challenging early in the course, if the presentation is atypical, or if a rare or non-neurodegenerative condition is present. This review equips community general neurologists to make an early provisional diagnosis before, or in place of, referral to a tertiary center. Early diagnosis would allay diagnostic uncertainty, allow prompt symptomatic management, provide disease-specific information and support resources, avoid further pointless testing and treatments, and create the possibility of trial referral. We address 64 APDs using one over-arching flow diagram and a series of detailed tables. Most instances of APDs can be diagnosed with a careful history and neurological exam, along with a non-contrast brain MRI. Additional diagnostic tests are rarely needed but are delineated where applicable. Our diagnostic algorithm encourages referral to a tertiary center whenever the general neurologist feels it would be in the patient's best interest. Our algorithm emphasizes that the diagnosis of APDs is an iterative process, refined with the appearance of new diagnostic features, availability of new technology, and advances in scientific understanding of the disorders. Clinicians' proposals for all diagnostic tests for the APDs, including repeat visits, should be discussed with patients and their families to ensure that the potential information to be gained aligns with their larger clinical goals. We designed this differential diagnostic algorithm for the APDs to enhance general neurologists' diagnostic skills and confidence and to help them address the less common or more ambiguous cases.

Commentary: Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.

Commentary: Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus.

Cloudburst Prediction System Using Machine Learning

Abstract: Cloudbursts are intense spells of rainfall and sudden occurrences resulting in much damage to the environment and loss of life. Hence predicting these events with high accuracy plays a critical role during such events. This paper presents systematic study on development and implementation of cloudburst prediction system utilizing a binary classification model. The primary aim of our project is to enhance predictive accuracy and reliability. In this manuscript, the authors propose a cloudburst prediction model that utilizes a multitude of ways for the forecast of cloudburst events based on a variety of features across India to predict real-time cloudburst disasters using certain cloudburst data.. The experiments conducted using logistic regression, SVM, and decision tree classifier models of machine learning. The SVM attained an initial stage of training AUC(Area Under Curve) ranging between 86% for training and 96% for testing. Discussion has been made concerning graphical result and comparison against other models. The system integrates with a weather API that will have real and updated meteoro-logical information like temperature, humidity, sea level pressure, dew point, cloud coverage, and wind speed. The results are then displayed on a web-based user interface that provides users the opportunity to interact with the prediction system

The Relationship between Self Efficacy and Dietary Approaches to Stop Hypertension (DASH) Diet and Blood Pressure of Hypertensive Patients in the Kediri City District

Hypertension is a prevalent, non-communicable disease with elevated blood pressure. Overconsumption of salt increases blood pressure, posing a risk of stroke and heart attack. The DASH diet aims to lower blood pressure and maintain normal levels. This study aims to explore the relationship between self-efficacy, DAHS diet, and blood pressure in hypertensive patients in the Kediri City sub-district. The study used a quantitative, observational, and cross-sectional approach to analyze the relationship between self-efficacy, the DASH diet, and blood pressure in hypertensive patients. A sample of 100 respondents was surveyed using questionnaires. The results showed that over half (70.0%) had high self-efficacy and adherence to the DAHS diet, while almost half (45.0%) had normal blood pressure. The chi-square test resulted in a p value of 0.000, rejecting the hypothesis.

The correlation between rapid eye movement sleep and nocturnal hypertension in patients with obstructive sleep apnea: A retrospective study.

Obstructive sleep apnea (OSA) is a respiratory disease closely associated with hypertension and heart disease. This study aimed to evaluate the diagnostic value of rapid eye movement (REM) sleep during nocturnal hypertension in OSA patients. We selected 194 patients who underwent polysomnography (PSG) at the Respiratory and Critical Care Medicine Department of Jiading District Central Hospital in Shanghai between January 2021 and August 2023. All patients were assigned to the hypertension and normal blood pressure groups, and the differences between the 2 groups were compared. This study included 194 patients (137 with nocturnal hypertension and 57 with normal nocturnal blood pressure). The proportion of male sex, body mass index, apnea-hypopnea index (AHI), REM sleep duration, percentage of REM sleep duration to total sleep duration, REM-AHI, nonrapid eye movement-AHI, oxygen desaturation index (ODI), REM-ODI, nonrapid eye movement-ODI, and average nighttime heart rate during sleep were higher in the hypertension group than in the normal blood pressure group, and the lowest oxygen saturation was lower than that in the normal blood pressure group (P < .05). Logistic regression analysis showed that REM-AHI was an independent risk factor for nocturnal hypertension (adjusted odds ratio, 1.048; 95% confidence interval, 1.011-1.086; P = .01). Receiver operating characteristic curve analysis showed that the REM-AHI had an area under the curve of 0.721 (95% confidence interval, 0.641-0.801; P < .001) for diagnosing nocturnal hypertension in patients with OSA, with a maximum Youden index of 0.379. The optimal critical value of the REM-AHI was 23.6 times/h, with a sensitivity of 64.2% and specificity of 73.7%. REM sleep is closely related to nocturnal hypertension, and patients with OSA are more prone to cardiovascular events.

Renal pseudohypoaldosteronism type 1-an adult case series including a novel gene variant.

Renal pseudohypoaldosteronism type 1 (PHA1) is a rare disease affecting infants. Symptoms are failure to thrive, vomiting, and weight loss. It is caused by gene variants in NR3C2 by which the mineralocorticoid receptor is dysfunctional, and patients develop hyponatremia, elevated plasma aldosterone, and renin but have normal blood pressure. Little is known about PHA1 in adults. We present four adults with PHA1, their clinical, biochemistry, and genetic data. Clinical and biochemical data were collected from the medical files and clinical examination of the participants. Genetic testing was performed. Two adult dizygotic twins and their mother, as well as an adult man were included. One of the sisters and the man had had severe hyponatremia and been admitted several times as young infants, treated with sodium chloride and fludrocortisone. All had as adults elevated plasma aldosterone and normal sodium. The females now had normal plasma renin, but it was increased in the male. A novel genetic variant in NR3C2 was found in the twins and their mother (c.1816T>C, p.(Cys606Arg)). All had normal blood pressure and were asymptomatic. In adulthood, PHA1 seems to be asymptomatic and long-term consequences favorable.

Pressure chamber fault diagnosis model design based on segmented control and adaptive fuzzy neural network

With the advent of the information age, the evolution of aerospace technology has rendered high-altitude flights increasingly common and vital. Nonetheless, the fault diagnosis of the pressure chamber, a crucial aspect of ensuring flight safety, remains an urgent challenge. The integration of segmented control technology in this domain further augments system stability and safety. This paper introduces a fault diagnosis model using EWTLM-FNN framework for monitoring and analyzing the state of the pressure chamber. The EWTLM-FNN framework commences with denoising and filtering of barometric pressure monitoring data to eliminate noise interference, followed by the extraction of frequency-domain modal information using the empirical wavelet transform (EWT). Subsequently, a three-layer Long Short-Term Memory Network conducts a profound analysis of the time and frequency domain features. The extracted features are then input into a fuzzy neural network (FNN) for fault identification and diagnosis, thus achieving high-precision monitoring of pressure chamber faults. Experimental results demonstrate that the proposed EWTLM-FNN framework exhibits superior fault diagnosis performance across multiple barometric pressure monitoring datasets, achieving over 90% diagnostic accuracy on the self-constructed pressure chamber fault dataset, and surpassing all indices compared to traditional machine learning and single deep learning models, thereby providing a theoretical and methodological foundation for future aircraft pressure fault diagnosis.