bluuebunny/crossref_metadata_2025 huggingface dataset:
G‐CSF—In Patients With Severe Alcohol‐Associated Hepatitis: A Real‐World Experience
Authors: Singh Ritu Raj , Chhabra Puneet , Dhillon Sonu
Published: October 2024

Background and Aims

Severe alcohol‐associated hepatitis (SAH) is associated with high short‐term mortality, and failure of response to corticosteroids is associated with a mortality of ~70%–80% within 6 months. Granulocyte colony‐stimulating factor (G‐CSF) has been studied in steroid non‐responders; however, the data are limited.

Methods

This is a multicentre retrospective cohort study. The study period was from January 2016 to November 2023. SAH was defined as alcohol‐associated hepatitis (ICD‐10‐CM codes) with serum bilirubin ≥ 5.0 mg/dL and INR ≥ 1.5. Other aetiologies of acute hepatitis and biliary obstruction were excluded. The primary outcome was 90‐day median overall survival in SAH patients treated with G‐CSF compared with standard medical therapy (SMT) or corticosteroids. Propensity score (1:1) matching was performed to control confounding variables.

Results

Among 20 132 patients with SAH, 10800 (53.65%) were treated with corticosteroids and 224 (1.11%) G‐CSF. The G‐CSF group was younger (45.5 vs. 48.4) White (79.91% vs. 72.40%); however, there was no age or gender difference between G‐CSF and corticosteroid groups. Whites and patients with more comorbidities received G‐CSF more frequently than SMT or corticosteroids. After propensity score matching, 90‐day overall survival was better in patients who received G‐CSF (88.31% vs. 62.36%, p < 0.01) compared with SMT or corticosteroids (88.31% vs. 74.39%, p < 0.01). Patients on G‐CSF had better 6‐month transplant‐free survival compared with SMT (83.53% vs. 55.36%, p < 0.001) or corticosteroids (82.89% vs. 60.21%, p < 0.001). Gastrointestinal bleeding was less common in G‐CSF group compared with corticosteroids (5.02% vs. 10.50%, p < 0.001).

Conclusions

A small minority of patients with severe alcohol‐associated hepatitis receive G‐CSF. G‐CSF improves 90‐day overall survival in patients with severe alcohol‐associated hepatitis and is non‐inferior to corticosteroids.
Development and Validation of a Scale to Measure the Perception of Workplace Gender Discrimination for Women in Nursing
Authors: Gauci Patricia , O'Reilly Kate , Luck Lauretta , You Wenpeng , Peters Kath
Published: October 2024

Workplace gender discrimination as perceived by women in nursing, is currently under researched. The aim of this article is to outline the development and validation of a scale designed to measure the perception of workplace gender discrimination for women nurses. The instrument was developed following a three‐stage process, consistent with recommendations for scale development. Twenty‐nine items were derived from two comprehensive literature reviews and interviews with 10 women nurses. Exploratory factor analysis (principal axis factoring) with varimax rotation was conducted for identifying the dimensions underlying the data set. All 29 items loaded onto a factor. Each factor included at least four items and all item loadings were well above the acceptable minimum value (> 0.40). The four factors collectively account for 71.27% of the total variance. Cronbach's alpha of the 29 items (0.964) showed good internal consistency. It is our hope that this research tool will not only be used within nursing, but also with some modification, in other women dominated professions.
Sensitive Hydraulic and Stomatal Decline in Extreme Drought Tolerant Species of California Ceanothus
Authors: Zailaa Joseph , Trueba Santiago , Browne Marvin , Fletcher Leila R. , Buckley Thomas N. , Brodersen Craig R. , Scoffoni Christine , Sack Lawren
Published: October 2024

Identifying the physiological mechanisms by which plants are adapted to drought is critical to predict species responses to climate change. We measured the responses of leaf hydraulic and stomatal conductances (Kleaf and gs, respectively) to dehydration, and their association with anatomy, in seven species of California Ceanothus grown in a common garden, including some of the most drought‐tolerant species in the semi‐arid flora. We tested for matching of maximum hydraulic supply and demand and quantified the role of decline of Kleaf in driving stomatal closure. Across Ceanothus species, maximum Kleaf and gs were negatively correlated, and both Kleaf and gs showed steep declines with decreasing leaf water potential (i.e., a high sensitivity to dehydration). The leaf water potential at 50% decline in gs was linked with a low ratio of maximum hydraulic supply to demand (i.e., maximum Kleaf:gs). This sensitivity of gs, combined with low minimum epidermal conductance and water storage, could contribute to prolonged leaf survival under drought. The specialized anatomy of subg. Cerastes includes trichomous stomatal crypts and pronounced hypodermis, and was associated with higher water use efficiency and water storage. Combining our data with comparative literature of other California species, species of subg. Cerastes show traits associated with greater drought tolerance and reliance on leaf water storage relative to other California species. In addition to drought resistance mechanisms such as mechanical protection and resistance to embolism, drought avoidance mechanisms such as sensitive stomatal closure could contribute importantly to drought tolerance in dry‐climate adapted species.
Sugars, Lipids and More: New Insights Into Plant Carbon Sources During Plant–Microbe Interactions
Authors: Zhang Qiang , Wang Zongqi , Gao Runjie , Jiang Yina
Published: October 2024

Heterotrophic microbes rely on host‐derived carbon sources for their growth and survival. Depriving pathogens of plant carbon is therefore a promising strategy for protecting plants from disease and reducing yield losses. Importantly, this carbon starvation‐mediated resistance is expected to be more broad‐spectrum and durable than race‐specific R‐gene‐mediated resistance. Although sugars are well characterized as major carbon sources for bacteria, emerging evidence suggests that plant‐derived lipids are likely to be an essential carbon source for some fungal microbes, particularly biotrophs. Here, we comprehensively discuss the dual roles of carbon sources (mainly sugars and lipids) and their transport processes in immune signalling and microbial nutrition. We summarize recent findings revealing the crucial roles of lipids as susceptibility factors at all stages of pathogen infection. In particular, we discuss the potential pathways by which lipids and other plant carbon sources are delivered to biotrophs, including protein‐mediated transport, vesicle trafficking and autophagy. Finally, we highlight knowledge gaps and offer suggestions for clarifying the mechanisms that underlie nutrient uptake by biotrophs, providing guidance for future research on the application of carbon starvation–mediated resistance.
A postcode lottery in education? Explaining regional inequality in multilevel systems
Authors: Schnabel Johanna
Published: October 2024

Inequality is a challenge for societies around the globe. In addition to its socio‐economic and political dimensions, inequality also has a territorial dimension. Focusing on educational inequality across regions, this article explores factors that shape regional differences in student enrolment and educational attainment in 14 OECD countries. The findings of the article suggest that a strong level of regional authority over education increases regional inequality in education. Further factors are regional differences in education expenditures and variation in the population size of regions. Low regional inequality in education seems to be explained by a low degree of regional authority or low differences in education spending. By examining regional inequality in education, the article enhances our understanding of the way multilevel governance influences the quality of life within regions. In particular, it highlights the impact of decentralization on territorial inequality.
A Dataset of 3M Single-DOF Planar 4-, 6-, and 8-Bar Linkage Mechanisms With Open and Closed Coupler Curves for Machine Learning-Driven Path Synthesis
Authors: Nurizada Anar , Dhaipule Rohit , Lyu Zhijie , Purwar Anurag
Published: October 2024

In recent years, there has been a strong interest in applying machine learning techniques to path synthesis of linkage mechanisms. However, progress has been stymied due to a scarcity of high-quality datasets. In this article, we present a comprehensive dataset comprising nearly three million samples of 4-, 6-, and 8-bar linkage mechanisms with open and closed coupler curves. Current machine learning approaches to path synthesis also lack standardized metrics for evaluating outcomes. To address this gap, we propose six key metrics to quantify results, providing a foundational framework for researchers to compare new models with existing ones. We also present a variational autoencoder-based model in conjunction with a k-nearest neighbor search approach to demonstrate the utility of our dataset. In the end, we provide example mechanisms that generate various curves along with a numerical evaluation of the proposed metrics.
Effect of Structure Parameters of 4D-Printed Actuators on Time-Depend Behavior: Experiment, Modeling and Design
Authors: Gao Yicong , Xu Chen , Zeng Siyuan , Tan Jianrong
Published: October 2024

Four-dimensional (4D) printing, an innovative advancement emerging from additive manufacturing, introduces a transformative approach to creating dynamic artifacts. This cutting-edge technology employs shape memory polymers as its core material, enabling the production of items that evolve and adapt over time in a pre-programmed manner. Central to this process is fused deposition modeling, a technique pivotal in realizing the self-assembly of functional structures that exhibit time-dependent behaviors. The essence of 4D printing lies in its ability to program artifacts for specific time-dependent deformations, a feature that demands meticulous control to avert any potential motion interference. This study delves into the intricate dynamics of a 4D-printed bilayer actuator, scrutinizing the interplay of various parameters that govern its time-dependent behavior. This study investigates the effects of parameters in terms of design parameter (the ratio of thickness) and printing parameters (printing speed, printing temperature and printing height) of a 4D-printed bilayer actuator. The results show that (i) The ratio of thickness emerges as a simple yet effective design principle crucial for controlling the speed of the actuator’s time-dependent self-folding mechanism. This insight opens up avenues for precision in designing 4D printed objects. (ii) The increase in printing speed leads to a corresponding increase in the speed of time-dependent deformation. In contrast, augmenting the printing temperature, layer height, or the overall height of the actuator yields a slowing effect on this time-dependent deformation process. (iii) The ratio of the two layers within the bilayer actuator influences the speed of time-dependent deformation in an opposing manner compared to other parameters. Building upon these findings, the study introduces a response surface model that intricately relates all the printing parameters to the time-dependent deformation behavior of the actuators. The model provides a comprehensive framework for understanding and manipulating the time-dependent deformation behavior of structures in 4D printing. By judiciously designing the bilayer actuators and optimizing the printing parameters, it becomes feasible to fabricate 3D printed structures that respond swiftly to thermal stimuli and exhibit a controlled, sequential, time-dependent shape-changing ability. These programmed time-dependent actuators play a crucial role in shaping the future of 4D printing technology, enabling the creation of structures that can automatically fold into predefined shapes in a time-controlled manner. These time-dependent functional capabilities mark an advancement in additive manufacturing, where adaptive dynamic materials can provide unprecedented lightweight, highly flexible solutions that offer new directions for innovative applications in fields as diverse as biomedical devices and aerospace engineering.
A Novel Approach for Optimizing Muscle Activation Level and Localization With Vibro-Acoustic DEA Feedback Capability
Authors: Chowdhury Tajbeed A. , Gratz-Kelly Sebastian , Wagner Eric , Motzki Paul , Lehser Martina
Published: October 2024

Thermal imaging is progressively being used nowadays to investigate a wide range of muscle disorders or irregularities causing increased or decreased blood flow. Furthermore, the activation of the muscle tissue can accurately be detected using electromyography technique by measuring the electrical activity produced during muscular contraction and relaxation. In combination with smart material-based actuators and sensors, which are versatile and appropriate for integration, a multisensor feedback unit for human interaction can be developed. This research presents a novel approach for investigating muscle activation level and localization using surface temperature based on low-cost surface electromyography and thermal sensor that incorporates multi-mode dielectric elastomer actuator feedback capability. We have implemented a deep learning algorithm to make prediction on muscle activation and localization for the purpose of optimization. A graphical user interface is developed to make predictions and forecast on the go. The deep learning model exhibits high evaluation scores across accuracy metrics with an accuracy of 92% on the test set. The study explores the potential applications of this approach in fields of biomechanics, rehabilitation, and human-machine interfaces.
Experimental System Identification of a Fully Coupled State Space Model for Mechanism-Free Ornithopters
Authors: Katibeh Mohammad , Shan Xin , Bilgen Onur
Published: October 2024

A piezoelectric-based mechanism-free ornithopter can eliminate the need for electromagnetic motors and conventional power transmission mechanisms, potentially saving weight and energy consumption, improving durability, and reducing overall mechanical complexity. In the previous research by the authors, the structural and fluid domains of the mechanism-free ornithopters were modeled by the Rayleigh-Ritz method and empirical state space Theodorsen function, respectively. The fully coupled system model is in state space form, which facilitates the application of parametric analyses in the preliminary design stage, multidisciplinary design optimization, and analyses of stability and controllability.

This paper aims to: (1) identify the unknown model coefficients and functions, and (2) validate the coupled state space model by using wind tunnel experiments. A prototype of the piezocomposite mechanism-free ornithopter wing is fabricated and subjected to testing in a low-speed wind tunnel. Each piezocomposite wing of the tested prototype is actuated by two Macro-Fiber Composite actuators bonded to a carbon fiber substrate. Initial simulation results indicate that this setup is particularly effective in replicating the heaving (bending) and pitching (twisting) motions characteristic of a natural flapping wing. The placement and alignment of these actuators were previously determined through analysis of the flapping wing model.

The wind tunnel experiments involve displacement and force measurements to validate the structural and fluid domain solutions, respectively. Aerodynamic load data is acquired using a six-degree-of-freedom load cell, and displacements are measured using 3D motion-capture camera system. The multipoint motion-capture technique facilitates for reconstructing a 3D representation of the motion of the flapping wing prototype. Tests are conducted across variations in key parameters such as flow speed, angle of attack, and excitation voltage magnitude, frequency, and phase of the excitation voltage between the two actuators. The experimental results are compared to the model predicted solution to evaluate the model’s accuracy and empirically obtain the system-level parameters under a wide range of operating conditions.
Online Health Monitoring of Electronic Components Subjected to Repeated High-Energy Shock
Authors: Satme Joud N. , Coble Daniel , Downey Austin R. J.
Published: October 2024

Electronic components that undergo shock and vibration are susceptible to failure caused by damage in the base printed circuit board that makes up the substrate of these systems. In certain applications, it may become paramount to know in real-time if the electronic components are damaged to enable a next-generation active system to take immediate responses. Broad examples of such systems include blast mitigation systems or safety systems in car accidents. These systems on classified under the term “high-rate” as they experience high shock levels on short time scales. This work proposes a long short-term memory neural network to enable real-time damage detection and assessment of electronic assemblies subjected to shock. The long short-term memory neural network is able to infer the state of the structure in approximately 4 milliseconds following the impact. The model obtains perfect classification results at 4 milliseconds for the data used in this work. This work is supported by experimentation that indicates damage to electronic packages can be quantified through the in situ monitoring of the impedance of electrical connections. Changes in impedance correlate to alterations in the physical properties of electronic components which indicate the occurrence of damage. On this basis, a comprehensive dataset is created to monitor the impedance changes of a daisy-chained connection through repeated high-energy shocks. Meanwhile, the shock response of the electronic components is captured using an accelerometer, enabling a detailed analysis of the effects of high-rate shock on the components’ performance. A dataset is developed to encompass 30 repeated impacts experiencing 10,000 gn during impact with an average half-sine time of 322 microseconds. The paper outlines the proposed real-time machine learning framework while performance metrics are presented and discussed in detail.
Development of a Bistable Fluid Valve Based on Shape Memory Alloys, Considering Production Using 3D Printing Processes
Authors: Pagel Kenny , Geißler Kevin , Erben Andreas , Drossel Welf-Guntram
Published: October 2024

Actuator systems based on shape memory alloys (SMA) offer many advantages compared to conventional drives: a significantly higher work density and the resulting lightweight design potential, simple design and noiseless operation Due to these special properties, SMA actuators are predestined for valve applications and have been particularly successful on the market in pneumatic systems for seat comfort applications. In the field of fluid valves, the first commercial products have recently been announced, addressing the areas of medical engineering and the food industry. What all known solutions have in common is that the systems are generally only activated for a very short time and are therefore usually designed as normally open or normally closed valves. SMA valves that can switch between two different states and keep them without energy are not yet known from the state of the art. One application for such valves is for example in irrigation systems. Here it is necessary to switch off the valves from time to time for a few hours a day to ensure that crops are watered as required and to avoid overwatering.

This paper presents the development of an additively manufactured fluid valve using a bistable mechanism activated by an SMA wire actuator. For this purpose, the state of the art for SMA-based valves is presented first. Furthermore, various known bistable mechanisms are analyzed and discussed regarding their suitability for valve systems and their manufacturability using 3D printing technology. Based on this, an additively manufacturable drive mechanism with a high degree of integration is being developed and the switching distances and forces are investigated by measurement. This load characteristics can be used to design a wire against spring arrangement to realize the drive task. This enables the bistable mechanism to be switched sequentially between the open and closed positions with a single SMA actuator wire using electrical activation. After deactivation, the mechanism holds the valve in an open or closed position without electrical energy. Using measurement results from long-term measurements, we also show that the actuating mechanism can realize the required 3650 switching cycles. A subsequent investigation of additively manufactured test specimens of the kinematics using the extrusion layer process and the stereolithography shows considerable differences in the manufacturing accuracy, which has an impact on the functional performance of the valve. Based on these investigations, recommendations for the manufacturing of such valves using additive manufacturing processes are formulated and their limitations are discussed.
Electromechanical Impedance-Based Defect Detection in Additively Manufactured Metal Parts in the Green State
Authors: Fawzy Mennatallah , El Halabi Mohamad , Rogers William C. , Albakri Mohammad I.
Published: October 2024

Additive Manufacturing (AM) plays a critical role in enabling the production of complex metal components with minimal waste. Bound Metal Deposition (BMD) is an AM technology that involves using metal powder suspended in a polymer binder to fabricate metal parts at relatively low temperature. At this stage, known as the green state, the binder plays a significant role in shaping the part. This process is followed by debinding and sintering stages, where the binder is removed, chemically and/or thermally, and metal particles are fused together. The latter stages are energy-intensive, time-consuming, and significantly contribute to the overall cost associated with the process. Given the nature of this technology, the as-printed manufactured parts in the green state are prone to defects during fabrication, which, if undetected, compromise part quality after the debinding and sintering stages. Thus, early detection of manufacturing defects and anomalies is critical for this technology, and can lead to significant time, cost, and energy savings.

This study investigates the feasibility of using electromechanical impedance measurements as a means for nondestructive testing (NDT) of additively manufactured metal parts, fabricated via BMD, at the green state. For this purpose, piezoelectric wafers are directly bonded to the parts under test. Owing to the coupled electromechanical characteristics of piezoelectric materials, the electrical impedance of the wafer reflects information about the part’s structural properties. Thus, the presence of a defect within the part can be detected by tracking variations in the impedance signature as compared to a defect-free part. A set of test specimens, representing defect-free and defective components are designed and fabricated as part of this work. Defect-seeded parts are created through deliberate CAD model modifications, adhering to ASTM standards. These CAD-seeded defects encompass vertical cylinders to mimic voids, feature shifts to mimic dimensional inaccuracies, and partial removal of layers from the part to mimic delamination or lack of fusion. Subsequently, the as-printed parts are instrumented, and impedance signatures are measured over the frequency range of 1–25 kHz. Variations in impedance signatures are quantified with standard damage metrics, including root-mean-square deviation and correlation coefficients, as well as through damage sensitive features in the signature. Based on this, optimal frequency ranges for defect detection are identified, taking into consideration uncertainties induced by the instrumentation process. The results demonstrate the capability of impedance-based NDT in detecting various types of defects at the green state.
Real-Time Shock Event Classification From Univariate Structural Response Measurements
Authors: Thompson Zhymir , Comert Gurcan , Satme Joud N. , Downey Austin R. J. , Bakos Jason D.
Published: October 2024

To enable real-time control of next-generation active structures during shock events, there is a need to identify the start of a shock event within microseconds of its initiation. The delayed classification of a shock event may cause damage to the system that could have been prevented with assumed next-generation active control mechanisms. Addressing the challenge of ultra-low latency shock event classification requires utilizing prior information on normal behaviors (i.e., the system under vibrational loading) to identify abnormalities that can be classified as features of a shock event. The purpose of changepoint shock classification is to automatically recognize when a structure of interest behaves differently than expected in some measurable way. In this work, we analyze two different methods for shock classification using changepoint methodologies. We study the use of adaptive cumulative summation and expectation maximization algorithms in this work. Each method presents advantages and disadvantages for different scenarios. This study aims to derive features (streams of time series data) for the changepoint algorithms and revise the changepoint models to be used in real-time robust shock event detection. In this work, a printed circuit board under continuous vibrations before, during, and after a shock event is used to investigate the proposed methodologies. The printed circuit board is monitored with an accelerometer that is used to monitor both the vibrational and shock state of the system. The vibrational response of the system consists of accelerations up to 20 m/s2, while the shock event consists of loadings up to 2,000 m/s2. This work showed that the CUSUM algorithm is fairly effective at identifying the shock state in data but generates many false positives during normal behavior times, with no false positives post-shock, indicating accurate shock state detection despite early errors. In contrast, the Expectation Maximization (EM) algorithm shows improved performance by correctly predicting no shock in the initial phase and accurately identifying the onset of the shock state. It occasionally misclassifies shocked points as normal due to its change point identification process. Compared to CUSUM, EM has fewer false positives before the shock and similar performance during and after the shock event. Future research efforts will focus on developing online versions of these algorithms, which can identify system states with a minimum number of errors. The limitations of the system and its robustness to noise are discussed.
Smart Adaptive Structures for an Ocean Wave Energy Converter
Authors: Niffenegger James , Boren Blake
Published: October 2024

Ocean wave energy converters face significant challenges including cost-effectiveness, minimizing maintenance requirements, and withstanding extreme conditions. However, by utilizing smart materials, these converters could overcome these challenges. Such energy harvesters could use dielectric elastomer generators to convert ocean wave energy into electricity through their dynamic straining. Conversely, by applying electricity to these generators, they become actuators — dielectric elastomer actuators — thereby enabling them to alter their stiffness and adapt to the ever-changing ocean energy environments. Such active adaptation could enhance the converter’s ability to: reach resonance with ocean waves and protect itself from dangerous waves.

This study utilizes numerical analyses through the COMSOL software framework to evaluate the potential energy that could be harvested by a conceptual ocean wave energy converter based upon dielectric elastomer generators/actuators. The converter is composed of an external hull (that is a hollow cylinder), an inertial mass (that is a hollow cylinder and concentric with the hull), and ‘spokes’ — made of dielectric elastomer generators/actuators — that connect the hull to the inertial mass. Results of the numerical analyses include those outcomes arising from the conceptual converter being simulated via a sinusoidal motion analogous to ocean waves. That motion, therefore, causes relative motion between the converter’s hull and inertial mass thereby dynamically stretching the corresponding connecting elastomers. The stretching of the elastomers enables them to ‘gain elastic strain energy’ and is, therefore, considered to be the theoretical limit of possible electrical energy conversion for the dielectric elastomer generator/actuator spokes. Additionally, the elastomer material properties of the spokes were altered to simulate the actuation of those same elastomers; with overall strain energy being subsequently investigated. Ultimately this is a preliminary study exploring the ability of such smart materials — electricity-generating and self-actuating elastomers — to actively adapt an ocean wave energy converter’s structure to address and overcome the aforementioned challenges.
Uneven Influx of European-Specific Alleles of SLC45A2, SLC24A5, TYRP1, DRD2, EDAR, and OCA2 Genes into the Gene Pool of the Koryaks
Authors: Malyarchuk B. A. , Litvinov A. N.
Published: October 2024

The distribution of alleles highly specific to Europeans in the Koryak gene pool, which formed as a result of intensive interethnic admixture in the Northern Priokhotie, characterized by the prevailing genetic contribution from males of Eastern European origin, was analyzed. The loci rs16891982 (SLC45A2 gene), rs1426654 (SLC24A5 gene), rs1408799 (TYRP1 gene), rs1076563 (DRD2 gene), rs3827760 (EDAR gene), and rs1448485 (OCA2 gene), which are mainly associated with the pigmentation system, were selected for analysis. High heterogeneity was found in the frequency of European-specific alleles, ranging from 1.4% for the variant rs1076563-C of the DRD2 gene to 14.7% for the variant rs1426654-A of the SLC24A5 gene. The reasons for the uneven influx of European-specific alleles into the Koryak gene pool are discussed. It is possible that the formation of genetic structure of modern Koryaks under intensive interethnic admixture was accompanied by the influence of natural selection on some parts of the genome.
Pancreatic cancer is feeling the heat
Authors: Maru Saumya Y , Jaffee Elizabeth M
Published: October 2024

Pancreatic adenocarcinoma (PDAC) is considered an immunologically ‘cold’ tumor that fails to attract or support effector T cells. Most PDACs are resistant to immune checkpoint blockade due to the complex signaling pathways that exist within its tumor microenvironment. Recent advances in genomic and proteomic technology advances are finally uncovering the complex inflammatory cellular and intercellular signals that require modulation and reprogramming. The goal is to ‘turn up the heat’ on PDACs with combination immunotherapies that incorporate T cell activating agents and immune modulatory agents, and successfully eradicate tumors. Here, we discuss progress and promising new research that is moving the field toward this goal.
Sand state parameter from CPT
Authors: Jung Hyunil , Lin Jiale , Stark Timothy D.
Published: October 2024

A cone penetration test (CPT)-based procedure is proposed to determine the in situ density and state parameter ( ψ) of cohesionless soils. To date several correlations have been developed to estimate ψ from CPT results, but most correlations are based on limited calibration testing and/or require soil constitutive parameters from laboratory shear tests. The new framework is based on a limit pressure calculated using cavity expansion theory and a semi closed-form solution that is scaled to existing calibration chamber data using a chamber shape factor. This enables estimation of ψ directly from CPT results, which makes it practical and theoretically sound. This facilitates determining ψ for use in flow failure assessment procedures for dams and embankments.
PreciPalm: An Intelligent System for Calculating Macronutrient Status and Fertilizer Recommendations for Oil Palm on Mineral Soils Based on a Precision Agriculture Approach
Authors: Seminar Kudang Boro , Imantho Harry , Sudradjat  , Yahya Sudirman , Munir Sirojul , Kaliana Indra , Mei Haryadi Fajar , Noor Baroroh Awalia , Supriyanto  , Handoyo Gani Cahyo , Kurnia Wijayanto Arif , Ijang Wahyudin Cecep , Liyantono  , Budiman Rhavif , Bakir Pasaman Achmad , Rusiawan Dwi , Sulastri
Published: October 2024

The measurement of the macronutrient values of an oil palm plantation is a complex and tedious task, particularly when dealing with large plantation areas. This situation complicates the process of the conventional measurement of nutrients by taking samples of oil palm leaves in the area being observed, causing delays in fertilizer recommendation and a lack of spatial diversity observation. Precision agriculture (PA) principles and approaches, which focus on assessing temporal and spatial variability, can be used to improve conventional measurement methods in terms of both accuracy and speed. This research aims to determine macronutrients, specifically nitrogen (N), phosphorus (P), and potassium (K) contents in oil palm leaves based on PA principles using the integration of remote sensing technology and machine learning to quickly obtain the macronutrient status from oil palm plantation areas. The Sentinel‐1A and Sentinel‐2A imagery data were analyzed and extracted to produce selected features, which are most influencing in the correlation between the imagery data and the leaf macronutrient values obtained from laboratory analysis. The random forest regression (RFR) model is used to produce correlation functions to compute macronutrient values. The use of the two satellites is to cope with cloud and smoke interference. The prototype system developed, named PreciPalm (Precision Agriculture Platform for Oil Palm), has been validated and implemented based on 2000 leaf sampling units representing several oil palm plantation areas in Indonesia, including Java, Sumatra, and Kalimantan. The observed system performance resulted in the measurement accuracy of 95.02%, 93.50%, and 82.52% for the nutrients N, P, and K, respectively. The novelty of PreciPalm is that it provides an ecosystem to transparently measure and observe the macronutrient status of oil palm in a timely, visual, spatial, and location‐specific manner, thereby improving oil palm nutritional management with more certainty and precision.
Biomarkers of Bacterial Translocation and Intestinal Wall Damage in Patients With Multiple Organ Dysfunction Syndrome
Authors: Turgunov Yermek , Ogizbayeva Alina , Asamidanova Sofiko , Avdiyenko Olga , Amanova Dana , Aukenov Nurlan , Mugazov Miras
Published: October 2024

Introduction: The aim of this study was to evaluate the potential biomarkers of bacterial translocation: lipopolysaccharide‐binding protein (LBP) and soluble CD14 subtype (sCD14‐ST), and intestinal wall damage: intestinal fatty acid binding protein (I‐FABP), zonulin, and regenerating islet‐derived protein‐3α (REG3α), in the patients with multiple organ dysfunction syndrome (MODS).

Methods: The study involved 78 patients over the age of 18 with MODS set according to the Sequential Organ Failure Assessment (SOFA) scale. Venous blood was sampled during diagnostics of MODS, on the 3rd and on the 7th day of its development. The sCD14‐ST, LBP, I‐FABP, REG3α, and zonulin in blood serum were determined by enzyme‐linked immunosorbent assay (ELISA).

Results: Out of 78 patients with MODS, 43 patients survived (55.1%) and 35 patients died (44.9%). Levels of sCD14‐ST on Day 1, I‐FABP on Day 3, and REG3α on Days 3 and 7 and SOFA scores on Days 1, 3, and 7, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores on Days 1, 3, and 7, and Modified Nutrition Risk in Critically Ill (mNUTRIC) scores on Days 1, 3, and 7 were statistically significantly higher in deceased patients (p < 0.05).

Conclusion: In patients with MODS, an increase in sCD14‐ST, I‐FABP, and REG3α in blood serum can indicate the violation of intestinal barrier function and increased bacterial translocation, which ultimately may aggravate the severity of MODS and increase the risk of death. It is required to further study the factors leading to intestinal wall permeability disorders in order to screen for timely intensive care measures that can help reduce the stay of patients in the intensive care unit (ICU) as well as mortality.

Trial Registration: ClinicalTrials.gov identifier: NCT06221293
Detecting and Predicting Models for QoS Optimization in SDN
Authors: Wassie Getahun , Ding Jianguo , Wondie Yihenew
Published: October 2024

Recently, deep learning algorithms and software‐defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers.
Computation and Experimentation on Astronaut Somatosensory Responses in a Lunar Environment
Authors: Yu Qihang , Wu Dianliang , Huang Shunzhou , Xu Hanzhong , Zhao Yue
Published: October 2024

In the lunar activities of the manned lunar landing project, it is necessary to calculate the astronauts’ somatosensory lunar driving operation according to the space mission verification requirements and the astronauts’ extravehicular operation training requirements. Considering the complexity and coupling among astronauts, lunar rovers, and the lunar environment, the measured driving data on the Earth’s surface cannot be applied to astronaut driving tasks on the lunar surface, and the control behaviors caused by different factors have limited adaptability to the lunar surface terrain and special mechanical environment. Under these circumstances, it is difficult to simulate the somatosensory effects of lunar rover driving training in the lunar environment. In response to the above problems, the astronaut–driving somatosensory calculation model is first described through the principle of human motion perception, the lunar environment is created in a virtual environment, the lunar gravity conditions are set, and a dynamic model of the astronaut‐lunar rover‐lunar surface system is created. Then, a dynamic calculation is used to provide parameters for the astronaut somatosensory calculation model, and the driving somatosensory model is simulated considering the astronauts’ own posture adjustment while driving. Finally, the experiment is designed and analyzed to determine the primary and secondary order of influence on the astronauts’ ability to drive somatosensory activity. The results show that the established driving motion perception model can provide an information model and data basis for astronauts’ lunar surface driving motion perception simulation training and can provide support for the development of manned lunar rovers and astronauts’ lunar surface simulation training.
miR‐320b, a Future Expected New Biomarker for Type 2 Diabetes Mellitus Induces Dysglycemia by Targeting PTEN
Authors: Wang Jinxingyi , Tao Ruyu , Hu Hanshuai , Gao Jiejie , Liu Yang , Xia Jie , Lan Xue , Di Yanan
Published: October 2024

Background: Type 2 diabetes mellitus (T2DM) has emerged as a global epidemic issue, with high rates of disability and fatality. Traditional diagnostic biomarkers are typically detected once a metabolic imbalance has already occurred, thus the development of early diagnostic biomarkers is crucial for T2DM. Metabolomics studies have identified several predictive biomarkers for T2DM, including miR‐320. Our previous research found that miR‐320b was significantly downregulated in T2DM patients, but the underlying mechanism remains unclear. Therefore, this study was designed to investigate the significance of miR‐320b for T2DM diagnosis and to explore the involved molecular mechanism.

Methods: A total of 50 patients with T2DM and 80 sex‐ and age‐matched healthy subjects were selected. The plasma miR‐320b of all participations was detected by qRT‐PCR and its correlations with other biomarkers of T2DM were analyzed. Besides, the expression of miR‐320b in HepG2 cells was suppressed by miRNA inhibitors. Then the glucose consumption of HepG2 cells was measured. The target gene of miR‐320b was predicted by four bioinformatics tools and intersected these prediction results by Venny method. The T2DM relevant target genes were identified by the GeneCards database. To ensure disease relevance, these T2DM relevant target genes were subsequently intersected with the target genes of miR‐320b. Protein–protein analysis (PPI) was used to screening the gene with the most connections in these target genes. Finally, the target gene of miR‐320b specific to T2DM was confirmed directly by luciferase reporter assay. The expression of target gene in HepG2 cell culture supernatant and plasma of all participations was detected.

Results: Our results showed that the expression level of miR‐320b was significantly lower in T2DM patients compared to the healthy controls. It was negatively correlated with fasting plasma glucose (FPG), glycated hemoglobin (HbA1C), and homeostasis model assessment of insulin resistance (HOMA‐IR), but positively with HOMA‐β. The glucose consumption of HepG2 cells in the miR‐320b inhibitor group was significantly lower compared to inhibitor‐NC and blank control group. We predicted and confirmed that phosphatase and tensin homolog (PTEN) was the direct target gene of miR‐320b using Bioinformation tools and luciferase reporter assay. Moreover, the concentration of PTEN was significantly higher in the HepG2 cell culture supernatant and plasma of T2DM patients.

Conclusions: Our research demonstrated a negative correlation between miR‐320b and FPG, HbA1C, and HOMA‐IR, while exhibiting a positive correlation with HOMA‐β. Suppressing miR‐320b expression would impair glucose consumption of HepG2 cells through PI3K pathway by targeting PTEN. These results suggest that miR‐320b may be a potential biomarker for diagnosing T2DM and a promising target for therapeutic intervention.
Noninvasive Assessment of Cardiopulmonary Hemodynamics Using Cardiovascular Magnetic Resonance Pulmonary Transit Time
Authors: Segeroth Martin , Winkel David Jean , Kaufmann Beat A. , Strebel Ivo , Yang Shan , Cyriac Joshy , Wasserthal Jakob , Bach Michael , Lopez-Ayala Pedro , Sauter Alexander , Mueller Christian , Bremerich Jens , Zellweger Michael , Haaf Philip
Published: October 2024

Introduction: Pulmonary transit time (PTT) is the time it takes blood to pass from the right ventricle to the left ventricle via the pulmonary circulation, making it a potentially useful marker for heart failure. We assessed the association of PTT with diastolic dysfunction (DD) and mitral valve regurgitation (MVR).

Methods: We evaluated routine stress perfusion cardiovascular magnetic resonance (CMR) scans in 83 patients including assessment of PTT with simultaneously available echocardiographic assessment. Relevant DD and MVR were defined as exceeding Grade I (impaired relaxation and mild regurgitation). PTT was determined from CMR rest perfusion scans. Normalized PTT (nPTT), adjusted for heart rate, was calculated using Bazett’s formula.

Results: Higher PTT and nPTT values were associated with higher grade DD and MVR. The diagnostic accuracy for the prediction of DD as quantified by the area under the ROC curve (AUC) was 0.73 (CI 0.61–0.85; p = 0.001) for PTT and 0.81 (CI 0.71–0.89; p < 0.001) for nPTT. For MVR, the diagnostic performance amounted to an AUC of 0.80 (CI 0.68–0.92; p < 0.001) for PTT and 0.78 (CI 0.65–0.90; p < 0.001) for nPTT. PTT values < 8 s rule out the presence of DD and MVR with a probability of 70% (negative predictive value 78%).

Conclusion: CMR‐derived PTT is a readily obtainable hemodynamic parameter. It is elevated in patients with DD and moderate to severe MVR. Low PTT values make the presence of DD and MVR—as assessed by echocardiography—unlikely.
Healthcare‐Associated Infections’ Characteristics Among Burn Patients and Risk Factors of Mortality: A Study Based on Data From a Tertiary Center in Iran
Authors: Mozafari Nima , Abbasi Montazeri Effat , Moogahi Sasan , Alavi Seyed Mohammad Amin
Published: October 2024

Background: Burn patients are more likely to get healthcare‐associated infections (HAIs). The current study aimed to investigate the characteristics of HAI and mortality risk factors among burn patients admitted to a tertiary center in Iran.

Methods: A retrospective study was conducted in 2021 on burn patients who developed HAI after hospitalization in a tertiary center in Ahvaz, Iran. The records of patients admitted and managed between March 2019 and March 2020 were reviewed. Statistical analysis was carried out using IBM SPSS Version 26, with p < 0.05 considered statistically significant.

Results: Of the 1,659 admitted patients, 395 (23.8%) developed HAI during the study period. After excluding patients with incomplete medical records and those with fungal or viral infections, 363 patients remained. The majority of these cases occurred in male patients. The most common type of HAI was burn wound infection (56.2%). Patients were mainly affected by Gram‐negative bacteria, Pseudomonas aeruginosa (39.7%), while the most common Gram‐positive bacteria was Staphylococcus epidermididis (9.6%). The most common antibiotic resistance among P. aeruginosa‐infected patients was reported against imipenem, followed by gentamicin and ciprofloxacin; however, the mentioned organism was mainly sensitive to colistin. Gender, age, bloodstream infection (BSI), ventilator‐associated infection (VAI), ICU admission, and total burned surface area (TBSA) resulted in 3.585, 1.028, 2.222, 7.469, 5.278‐, and 1.031 times higher mortality rates, respectively.

Conclusion: Female gender, advanced age, BSI, VAI, and ICU admission are risk factors for HAI. These findings emphasize the need for focused infection prevention and management to improve high‐risk burn patient survival.