Traditional Tests Research Articles

Efficient machine learning for strength prediction of ready-mix concrete production (prolonged mixing)

Purpose This paper aims to clarify the efficient process of the machine learning algorithms implemented in the ready-mix concrete (RMC) onsite. It proposes innovative machine learning algorithms in terms of preciseness and computation time for the RMC strength prediction. Design/methodology/approach This paper presents an investigation of five different machine learning algorithms, namely, multilinear regression, support vector regression, k-nearest neighbors, extreme gradient boosting (XGBOOST) and deep neural network (DNN), that can be used to predict the 28- and 56-day compressive strengths of nine mix designs and four mixing conditions. Two algorithms were designated for fitting the actual and predicted 28- and 56-day compressive strength data. Moreover, the 28-day compressive strength data were implemented to predict 56-day compressive strength. Findings The efficacy of the compressive strength data was predicted by DNN and XGBOOST algorithms. The computation time of the XGBOOST algorithm was apparently faster than the DNN, offering it to be the most suitable strength prediction tool for RMC. Research limitations/implications Since none has been practically adopted the machine learning for strength prediction for RMC, the scope of this work focuses on the commercially available algorithms. The adoption of the modified methods to fit with the RMC data should be determined thereafter. Practical implications The selected algorithms offer efficient prediction for promoting sustainability to the RMC industries. The standard adopting such algorithms can be established, excluding the traditional labor testing. The manufacturers can implement research to introduce machine learning in the quality controcl process of their plants. Originality/value Regarding literature review, machine learning has been assessed regarding the laboratory concrete mix design and concrete performance. A study conducted based on the on-site production and prolonged mixing parameters is lacking.

Clinical characteristics and risk factors for mortality in pneumonia-associated acute respiratory distress syndrome patients: a single center retrospective cohort study.

Pathogenic diversity may have contributed to the high mortality of pneumonia-associated acute respiratory distress syndrome (p-ARDS). Metagenomics next-generation sequencing (mNGS) serves as a valuable diagnostic tool for early pathogen identification. However, its clinical utility in p-ARDS remains understudied. There are still limited researches on the etiology, clinical characteristics and risk factors for 28-day mortality in p-ARDS patients. A single center retrospective cohort study of 75 p-ARDS patients was conducted. Patients were categorized into survival and deceased groups based on their 28-day outcomes. A comprehensive clinical evaluation was conducted, including baseline characteristics, laboratory indicators, outcomes and pathogen identification by mNGS and traditional microbiological testing. We then evaluated the diagnostic value of mNGS and identified clinical characteristics and risk factors for 28-day mortality in p-ARDS. The overall ICU mortality was 26.67%, and the 28-day mortality was 57.33%, with 32 cases (42.67%) in the survival group, and 43 cases (57.33%) in the deceased group. Patients in the deceased group were older than those in the survival group (68(59,73) years vs. 59(44,67) years, P=0.04). The average lengths of ICU and hospital stay were 9(5,13) days and 14(7,21) days, respectively. The survival group had longer lengths of ICU and hospital stay (ICU: 11(7,17) days and hospital: 17(9,27) days) compared to the deceased group (ICU: 8(4,11) days and hospital: 12(6,19) days) (P<0.05). Survival patients exhibited lower Acute Physiology and Chronic Health Evaluation (APACHE) II score on the 3rd and 7th days, higher lymphocyte counts, higher CD3+ and CD8+ T cell counts compared to deceased patients (P<0.05). Multivariate logistic regression analysis identified age, APACHE II scores on 3rd and 7th days, CD8+ T cell count and length of ICU as independent risk factors for 28-day mortality in p-ARDS patients. mNGS demonstrated a significantly higher overall pathogen detection rate (70/75, 93.33%) compared to the traditional method (50/75, 66.67%, P=0.022). The average turnaround time (TAT) for mNGS was significantly shorter at 1(1,1) day compared to 4(3,5) days for the traditional method (P<0.001). Metagenome next-generation sequencing can be used as a valuable tool for identifying pathogens in p-ARDS, reducing diagnostic time and improving accuracy. Early application of mNGS alongside traditional methods is recommended for p-ARDS. Furthermore, older age, higher APACHE II scores, lower lymphocyte counts and lymphocyte subset counts were associated with increased mortality in p-ARDS patients, highlighting the importance of timely assessment of immune status and disease severity, especially in elderly.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Sample-to-answer direct real-time PCR detection of Anaplasma phagocytophilum, Ehrlichia spp., and Babesia spp. infections in whole-blood specimens.

This work demonstrates that detection of tick-borne illnesses, such as anaplasmosis, babesiosis, or ehrlichiosis, can be performed directly from whole blood with no extraction. The assay described here has a high positive and negative percent agreement with existing methods and is used as the standard of care. An increasing incidence of tick-borne illness combined with shortage of well-trained technologists to perform traditional manual testing, testing options that can be adapted to various lab settings, are of the utmost importance.

Organizational Commitment, Work Motivation, and Job Satisfaction: Understanding Its Impact of Employee Performance (Case Study at Bank Mandiri (Persero)

The objective of this study is to examine the impact of organizational commitment, motivation, and job satisfaction on employee performance. The sample population for this study consists of a total of 35 people employed by the company. The sampling technique employed in this study is non-probability sampling. This study employs validity and reliability tests to assess the questionnaires, as well as traditional assumption tests. Additionally, multiple linear regression analysis is conducted. The results of this study show that at Bank Mandiri (Persero), employee performance is positively and significantly impacted by organizational commitment, work motivation, and job satisfaction.

Effective comparison of two potentially crossing hazard rate curves

In survival data analysis, comparison of two hazard rate curves is critically important for evaluating a treatment effect. In many applications, the two hazard curves could potentially cross each other, violating the proportional hazards assumption in the Cox's model. In such cases, the traditional tests like the log-rank test and the Peto-Peto test that were developed based on that assumption would be ineffective. There have been some discussions in the literature on comparison of two potentially crossing hazard curves, based on either parametric modeling or nonparametric testing approaches. However, the assumed models of the existing parametric methods are often difficult to justify in practice. On the other hand, the existing nonparametric tests are usually based on the maximization with respect to an unknown crossing point, leading to complex null distributions for the corresponding test statistics. We suggest a novel method in this paper for comparing two hazard curves based on a nonparametric testing procedure. Its test statistic avoids the maximization mentioned above and consequently has the desirable asymptotic normality property under some regularity conditions. We show that the new method is effective for comparing two potentially crossing hazard curves.

Characterizing fabric crease recovery through sequential image analysis

Abstract Crease recovery is the ability of a fabric to revert to its original condition after deformation or folding. This recovery process is intricately linked to several fabric properties, including fiber content, yarn structure, weave, fabric finish, and mechanical treatments. Based on the dynamic nature of crease recovery, this paper employs sequential image analysis to track the velocity of fabric crease recovery at different positions and extract simple metrics for measuring fabric shape retention. In each image, the contour of the creased sample is detected, and the contour is modeled by a Gaussian function to calculate its barycenter. The barycenter of a crease is the point in space where the mass of the crease is concentrated, reflecting the shape and position of the crease. During the recovery process, the translation of the barycenter of the creased sample can be determined from the sequential images, leading to the calculation of crease's recovery velocity. Experimental results demonstrate a linear relationship between the barycentric velocity and logarithmic time. The slope of the resulting fit line, designated as the crease coefficient k, serves as a singular metric for assessing the fabric's shape retention following the release of the crease. This methodology is benchmarked against traditional fabric crease behavior tests, including the draping coefficient, bending length, and crease recovery angle. It demonstrates that the crease coefficient k offers greater reliability and accuracy across tests on 10 diverse fabric samples, which varied in terms of fiber content, weave, yarn size, and density.&amp;#xD;

Program Segment Testing for Human–Machine Pair Programming

Human–Machine Pair Programming (HMPP) is a promising technique in the software development process, which means that software construction can be done in the manner that humans are responsible for developing the program while computer is responsible for monitoring the program in real-time and reporting errors. The Java runtime exceptions in the current version of the software under construction can only be effectively detected by means of its execution. Traditional software testing techniques are suitable for testing completed programs but face a challenge in building a suitable testing environment for testing the partial programs produced during HMPP. In this paper, we put forward a novel technique, called Program Segment Testing (PST) for automatically identifying errors caused by runtime exceptions to support HMPP. We first introduce the relevant involved in this technique to detect index out of bounds exceptions, a representative of runtime exceptions. Then we discuss the methodology of this technique in detail and illustrate its workflow with a simple case study. Finally, we carry out an experiment to evaluate this technique and compare it with three existing fault detection techniques using several programs to demonstrate its effectiveness.

Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review.

Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed.

Integrating Patient-Reported Outcomes in the Evaluation of Breast Implant Satisfaction and Quality of Life

With a focus on aesthetic and reconstructive services, PROs have become even more important in assessing the success rate of procedures. More specifically, the current study looks into the use of PROs to gauge patient quality of life and satisfaction with breast implants. Accordingly, patients may not be content with their care because typical measures based on a proportion of fatalities, morbidity/mortality rates, or other consequences following treatment do not necessarily provide the complete picture. It is recognized that including PROs could assist healthcare practitioners with patient satisfaction insights that conventional measurements must precisely record. Among the frequently employed tools is the Breast-Q, which assesses many domains, such as satisfaction with the physical, psychological, and sexual aspects of the breast's appearance. Because of this, research has shown that incorporating these indicators is crucial for evaluating the perceived surgical outcomes, which show improved life quality and psychosocial elements and enhanced satisfaction with the appearance of the treated body area. However, physical discomfort and other adverse effects might sometimes be felt as tightness or pain that are difficult to quantify with traditional medical testing. When PROs are used in the clinic, a more thorough picture of the patient's functioning domains is provided, which enables the doctor to help the patient manage their post-operative pain or consult with a specialist about psychiatric issues. PROs can also strengthen the relationship between patients and clinicians by allowing patients to participate in creating their treatment plans, which guarantees that the plans are tailored to the patients' needs and preferences. Therefore, we must stress the significance of PROs, especially in assessing patient satisfaction with breast implants and their overall quality of life. Carotenemia SC identified areas of strength and need for improvement from the perspective of subjective constructs to enhance process quality improvement in breast implant surgery for the benefit of patients.

Emerging Methods in the Identification of Bacterial Respiratory Tract Pathogens.

Here, we will review different bacterial causes of respiratory tract infections and discuss the available diagnostic methods. Moreover, we will provide some recently published patents and newer techniques, such as respiratory panels and omics approaches, and express the challenges in this path. Respiratory tract infections (RTIs) include those infections that can lead to the involvement of different respiratory parts, including the sinuses, throat, airways, and lungs. Acute respiratory tract infection is the leading cause of death from infectious illnesses worldwide. According to the World Health Organization, 1.6 to 2.2 million deaths have occurred due to acute respiratory infections in children under five years of age. About 4 million people die annually from respiratory infections, 98% of which are caused by lower respiratory infections. Depending on the type of pathogen, the severity of the infection can vary from mild to severe and even cause death. The most important pathogens involved in respiratory tract infections include Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis. The symptoms are often similar, but the treatment can vary greatly. Therefore, correct diagnosis is so important. There are several methods for diagnosing respiratory infections. Traditional tests include the culture of respiratory samples, considered the primary tool for diagnosing respiratory infections in laboratories, and less common standard tests include rapid and antigenic tests. It is essential to think that the culture method is reliable. In the original method of diagnosing respiratory infections, some bacteria were challenging to grow successfully, and many clinical laboratories needed to be equipped for viral cultures. Another issue is the time to get the results, which may take up to 7 days. Rapid and antigenic tests are faster but need to be more accurate. The clinical laboratories are trying to be equipped with molecular methods for detecting respiratory pathogens and identifying the genetic material of the infectious agent in these new methods as the primary method in their agenda.

P-545 Early pregnancy outcomes associated with age and AMH following Next-Generation Sequencing(NGS)-based Preimplantation Genetic Testing for Aneuploidy(PGT-A) in single euploid embryo transfers for advanced age patients

Abstract Study question Does age and AMH impact early pregnancy outcomes in euploid single embryo transfers using NGS-based PGT-A for individuals aged 35 and above? Summary answer In this NGS-based PGT-A study, no significant differences in early pregnancy rates were observed based on age or AMH among individuals aged 35 and above. What is known already A recent meta-analysis suggest a decline in pregnancy rates with advancing age in the context of euploid embryo transfer following PGT-A. However, it’s crucial to acknowledge variations in PGT-A techniques across studies. While individuals with low AMH levels may encounter challenges in obtaining euploid embryos, the precise impact of AMH on pregnancy rates post-embryo acquisition remains unclear. Study design, size, duration A retrospective cohort study was conducted on 428 cycles of single euploid embryo transfers in patients aged 35 and above, performed at the single infertility center from January to December 2022. Participants/materials, setting, methods The study examined frozen-thawed embryo transfer cycles in patients aged 35 and above, analyzing early pregnancy outcomes in single euploid embryo transfers. Participants were grouped by age(35-38, 38-42, 42 and above) and AMH levels(&amp;lt;1, 1-3, &amp;gt;3 ng/ml). NGS-based PGT-A conducted embryo biopsy. The analysis aimed to detect differences in initial pregnancy rates among age and AMH subgroups. The differences between groups were compared using Pearson’s chi-squared test. Main results and the role of chance The average age at transfer was 39.05 ± 2.53 years, with a mean AMH of 2.60 ± 1.86(ng/ml). The mean number of prior frozen embryo transfer (FET) cycles was 1.9 ± 2.04, with 119 patients (27.8%) having undergone three or more transfer cycles. Transfer cycles comprised 363 (84.8%) artificial cycles and 65 (15.2%) ovulatory cycles, with an average endometrial thickness of 8.95 ± 1.73 mm at the time of transfer. The β-hCG positive rate was 57.7% (247/428), and the clinical pregnancy rate was 53.5% (229/428). When analyzing positive b-hCG results by age, rates were 59.9% (82/137) for ages 35-38, 55.5% (121/218) for ages 38-42, and 60.3% (44/73) for ages 42 and above (p = 0.641). In terms of AMH, β-hCG positive rates were 52.1% (37/71) for &amp;lt;1, 59.1% (120/203) for 1-3, and 58.0% (83/143) for ≥3, with no statistically significant differences (p = 0.584). Clinical pregnancy rates were 54.0% for ages 35-38, 51.8% for ages 38-42, and 57.5% for ages 42 and above. For AMH, rates were 50.7% for &amp;lt;1, 53.2% for 1-3, and 54.5% for ≥3, with no statistically significant differences (p = 0.869). Limitations, reasons for caution Being a retrospective study analyzing patients undergoing embryo transfer, our study overlooks variables related to oocyte retrieval. Given the challenges of obtaining euploid embryos in advanced age, further investigation is warranted to explore the probability and duration of embryo acquisition in this population. Wider implications of the findings In this study, utilizing NGS-based PGT-A after generating euploid embryos, no significant differences in early pregnancy outcomes were observed for women aged 35 and older, considering age and AMH levels. The study suggests further research is needed to explore if NGS-based PGT-A can address limitations of traditional chromosomal testing methods. Trial registration number not applicable

Advancements in integrated robotic sensing: A European perspective

Robotic Non-destructive Testing and Sensing stands at the forefront of technological innovation, offering capabilities in assessing structural integrity, safety, and material quality across diverse industries. This comprehensive review article provides a detailed exploration of the field, focusing on the substantial contributions of European researchers and institutions. The need for non-destructive testing has been a constant in industries that rely on structural integrity, including aerospace, manufacturing, energy, construction, and healthcare. Traditional testing methods, such as radiography, ultrasonic testing, magnetic particle testing, and dye penetrant testing, have been integral for quality control and safety assurance. However, the robotisation of such methods has marked a profound shift, enabling precise, fast, efficient, and repeatable testing while minimising human exposure to hazardous environments. European researchers and institutions have played an instrumental role in driving the evolution of integrated robotic sensing. The historical perspective of the field reveals the pioneering spirit of Europe, as collaborative initiatives led to the development of robotic platforms equipped with advanced sensors and testing techniques. A critical aspect of the European impact on robotic inspection applications lies in developing advanced sensors, innovative robotic platforms, novel robotic path-planning and control approaches and data collection and visualisation tools. These developments continue to influence the global landscape of robotic sensing. European researchers remain at the forefront of current trends and innovations as the field continues to evolve. This review article will delve into these recent advancements, highlighting Europe’s pivotal role in pushing the boundaries of technology and application. The implications and applications of robotic sensing reverberate across multiple sectors worldwide. From inspecting critical aerospace components to ensuring the quality of manufactured goods, these technologies underpin safety and quality standards.

Improving safety: Neuraxial blockade guided by thromboelastography for patients with uncertain coagulation profile.

Neuraxial blockade procedures are essential for anesthesia and pain management but pose risks in patients with uncertain coagulation profiles. Traditional coagulation tests often fail to predict bleeding risks associated with neuraxial blockade. Thromboelastography (TEG) offers real-time insights into coagulation status, potentially improving safety outcomes. In this case series, six patients underwent neuraxial blockade guided by TEG analysis. An individualized anesthetic plan was formulated based on TEG findings to mitigate bleeding risks while ensuring pain management. Tailoring anesthetic techniques to real-time TEG data improved safety outcomes with minimized bleeding complications and satisfactory pain control. In conclusion, neuraxial blockade guided by TEG enhances safety in patients with uncertain coagulation profiles. Further studies are needed to validate benefits in broader clinical settings.

Challenges and Successes in Introducing Coronary CT Angiography in an Emergency Medicine-Run Observation Unit.

This study was designed to analyze the departmental changes in transitioning the Emergency Department (ED)-run Observation Medicine Unit's routine noninvasive cardiac evaluation from the traditional standard-of-care procedures to coronary computed tomography angiography (CCTA). While the routine use of CCTA for the evaluation of chest pain has been deemed feasible and safe, provider confidence appears apprehensive, and ordering patterns appear reluctant to change. We conducted a retrospective analysis of data from two risk-matched cohorts of EDpatients who presented with symptoms suggestive of acute coronary syndrome (ACS) but without ischemic electrocardiogram (ECG) changes or positive troponin. Endpoints included length of stay, major adverse cardiovascular event (MACE) rates at 28 days, recidivism rate, and downstream findings on coronary catheterization. The adoption of CCTA led to a significant reduction in the length of stay for patients in the ED-run Observation Medicine Unit. Provider and nursing education initiatives were crucial in overcoming initial resistance and improving the implementation of CCTA. Post-education, there was a marked increase in the volume of CCTA performed and a decrease in the length of stay, enhancing overall departmental throughput. The results suggest that CCTA offers a reliable and efficient diagnostic alternative to traditional noninvasive tests, with high diagnostic accuracy contributing to faster decision-making and reduced need for invasive procedures. Continuous education for providers and nursing staff was essential to ensure adherence to the new protocol and improve clinical outcomes. Transitioning to CCTA for routine noninvasive cardiac evaluation in the ED-run Observation Medicine Unit demonstrated significant efficiency and diagnostic accuracy benefits. Successful implementation requires targeted educational efforts to ensure competency and confidence among healthcare providers. The findings support the integration of CCTA into standard clinical practice for the evaluation of chest pain in the emergency setting, with future research needed to validate these results in broader patient populations and assess long-term outcomes.

Electrophysiological correlation between executive vigilance and attention network based on cognitive resource control theory

Attention is comprised of three independent and interacting attention networks: phasic alertness, orienting, and executive control. Previous studies have explored event-related potentials associated with these attention networks and executive vigilance, there is a lack of research on the relationship between executive vigilance and the three attention networks. However, there is a lack of research on the relationship between executive vigilance and the three attention networks. The present study aims to investigate this relationship. Based on the theory of cognitive resource control, two experimental blocks were designed with the vigilance task as the control variable. A total of 39 participants completed both ANTI and ANTI-V trials (two variants of the traditional attention network test ANT) in the same period. Through analysis of behavior measures (RT) and electrophysiological results related to phasic alertness (N1, P2, and contingent negative variation), orienting (P1, N1, and P3), and executive control (N2 and slow positive potential), we found that the reaction time of the ANTI block was lower than that of the ANTI-V block under all conditions, This suggests that adding a vigilance task may lead to reduced allocation of attention resources across all three attention networks. Furthermore, the orienting ability was weaker in the ANTI-V experimental block compared to that in the ANTI block due to effects on P1 and P3 regulation by the vigilance task. The N2 amplitude of the ANTI-V block was consistently reduced under similar conditions, indicating a weakening of executive control ability. The electrophysiological results revealed that executive vigilance inhibited the component of early attention perception related to the orienting network and was also related to the ability to detect conflict in the executive control network.

Paper 23: Functional Stability Testing and Athletic Shoulder Performance Testing Higher in Patients Able to Return to Sport Following Arthroscopic Shoulder Stabilization

Objectives: There is a paucity of literature on return to sport functional testing following arthroscopic labral repair for shoulder instability leading to uncertainty of when it is safe to return to sport. Currently, the most commonly used evaluation metrics are time after surgery and less commonly range of motion and strength of operative extremity. There is currently no validated return to sport testing protocol after arthroscopic labral repair. The aim of this study was to compare elements of a proposed return to sport battery for those undergoing arthroscopic Bankart repair between those able to return to play and those who were not able to by 6 months. Methods: All patients who participated in athletics who underwent arthroscopic labral repair for shoulder instability from June 2021 to December 2022 with a fellowship trained orthopedic surgeon were eligible to participate. Study protocol was to undergo functional testing at 4 months post operatively and every 2 months thereafter until cleared for athletics. Functional testing included Closed Kinetic Chain Upper Extremity Stability Test (CKCUES), Posterior Shoulder Endurance Test (PSET), Strength via handheld dynamometry at various range of motion positions. The Athletic Shoulder Test (ASH) has two components- peak force in I, T, Y position (PEAK), and rate of force development in I, T, Y position (RATE). Absolute value as well as limb symmetry index (LSI) were compared. For each test, passing was determined to be greater than 90% symmetry with contralateral extremity. Tests that had a greater than 20% difference in pass rates between the not return to play group (NRTP) and the return to play (RTP) groups were considered to be predictive. Results: Overall, 22 patients with at least 6 months of follow up, completed functional testing battery, and cleared to return to play were included. Mean age was 19.8 years (range 11-44 years), with 12 males and 10 females, and the mean follow up was 11 months (6-24 months). Seventeen returned to play (RTP) at mean 6.3 months (range 4.5-23.1), five did not to return (NRTP). Mean SANE score was higher in RTP vs NRTP, 61 (range 25-95) vs 86.7 (60-100), p=0.03). Those that returned to sport met a mean of 6.25 of the 12 criteria at 4 months compared to 4.20 of those that did not return to sport (p-value = .22). At 4 months, RTP group passed several tests at higher rates than NRTP group - CKCUES (78% vs 0%), PEAK (62.5% vs 20%), RATE (50% vs 0%), and Shoulder Flexion strength (88.9% vs 50%). Other strength measurements were not significantly different between groups. Conclusions: CKCUES and ASH (PEAK and RATE) performance testing criteria were significantly higher at 4 months in patients who are able to eventually be cleared to return to sport compared to those that are not able to. Traditional strength testing did not demonstrate differences between groups. Therefore, we recommend that CKCUES testing and ASH testing with force plates should be incorporated in functional testing as early as 4 months.

Significant Improvements to Si Calendar Lifetime Using Rapid Electrolyte Screening via Potentiostatic Holds

Silicon-based lithium-ion batteries exhibit severe time-based degradation resulting in poor calendar lives. This has been identified as the major impediment towards commercialization with cycle life considered a solved issue through nanosizing and protective coatings allowing over 1000 cycles of life to be achieved. In this work, rapid screening of sixteen electrolytes for calendar life extension of Si-rich systems (70 wt% Si) is performed using the voltage hold (V-hold) protocol. V-hold significantly shortens the testing duration over the traditional open circuit voltage reference performance test allowing us to screen electrolytes within a span of two months. We find a novel ethylene carbonate (EC) free electrolyte formulation containing lithium hexafluorophosphate (LiPF6) salt, and binary solvent mix of fluoroethylene carbonate (FEC), ethyl methyl carbonate (EMC) that extends calendar life of Si cells as compared to conventional EC based electrolyte. Our coupled experimental-theoretical analysis framework provides a decoupling of the parasitic currents during V-hold, allowing us to extrapolate the capacity loss to predict semiquantitative calendar lifetimes. Subsequently, cycle aging and oxidative stability tests of the EC free system also show enhanced performance over baseline electrolyte.

Utility of Global Hemostatic Assays in Patients with Bleeding Disorders of Unknown Cause.

Bleeding disorder of unknown cause (BDUC) is a diagnosis of exclusion after exhaustive evaluation of plasmatic coagulation and platelet function. This review explores the utility of global hemostatic assays as confirmatory tests and in elucidating the pathophysiology of BDUC. Unlike traditional hemostatic tests that focus on coagulation factors, global assays are conducted both in plasma and also whole blood. These assays provide a more comprehensive understanding of the cell-based model of coagulation, aid in the identification of plasmatic factor abnormalities that may reduce hemostatic capacity, and allow for the assessment of impaired platelet-endothelial interactions under shear stress, as well as hyperfibrinolytic states. While clinical tests such as skin bleeding time and global assays such as PFA-100 exhibit limited diagnostic capacity, the role of viscoelastic testing in identifying hemostatic dysfunction in patients with BDUC remains unclear. Thrombin generation assays have shown variable results in BDUC patients; some studies demonstrate differences compared with healthy controls or reference values, whereas others question its clinical utility. Fibrinolysis assessment in vitro remains challenging, with studies employing euglobulin clot lysis time, plasma clot lysis time, and fluorogenic plasmin generation yielding inconclusive or conflicting results. Notably, recent studies suggest that microfluidic analysis unveils shear-dependent platelet function defects in BDUC patients, undetected by conventional platelet function assays. Overall, global assays might be helpful for exploring underlying hemostatic impairments, when conventional hemostatic laboratory tests yield no results. However, due to limited data and/or discrepant results, further research is needed to evaluate the utility of global assays as screening tools.

Real-time hybrid test method for floating wind turbines: Focusing on the aerodynamic load identification

Since the development of floating wind turbine (FWT) shows a rapid trend towards larger capacity and larger rotor size, the traditional full-model basin test method has encountered limits. Especially the balance between the wind generation system (WGS) size and scale ratio of the model FWT system. Under such circumstances, the hybrid model test method provides the possibility to improve the FWT model test. In the hybrid model test method, the original physical model system is divided into physical subsystem and numerical subsystem, and the data acquisition and process module between the 2 subsystems plays an important role. In this paper, a framework of real-time hybrid test (RTHT) system is setup firstly, which combines physical model wind turbine and motion platform. The damping modification to the corresponding numerical code is applied to improve the motion calculation accuracy. Delay implementation is employed to avoid motion divergence. Then a simulation loop is setup in numerical environment to study the identification of aerodynamic load. The influence of identification accuracy to the RTHT result is analyzed. Lastly, the dual-accelerometer method of aerodynamic load identification is employed in the proposed RTHT system. Decay tests and irregular wave only tests are carried out to validate the aerodynamic load identification method. The capability and potential of the RTHT method of floating wind turbine model test is preliminary proved in the work of this paper.