bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
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
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.